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Estimation of vulnerable zones due to accidental release of toxic materials resulting in dense gas clouds.

Heavy gas dispersion models have been developed at IIT (hereinafter referred as IIT heavy gas models I and II) with a view to estimate vulnerable zones due to accidental (both instantaneous and continuous, respectively) release of dense toxic material in the atmosphere. The results obtained from IIT heavy gas models have been compared with those obtained from the DEGADIS model [Dense Gas Dispersion Model, developed by Havens and Spicer (1985) for the U.S. Coast Guard] as well as with the observed data collected during the Burro Series, Maplin Sands, and Thorney Island field trials. Both of these models include relevant features of dense gas dispersion, viz., gravity slumping, air entrainment, cloud heating, and transition to the passive phase, etc. The DEGADIS model has been considered for comparing the performance of IIT heavy gas models in this study because it incorporates most of the physical processes of dense gas dispersion in an elaborate manner, and has also been satisfactorily tested against field observations. The predictions from IIT heavy gas models indicate a fairly similar trend to the observed values from Thorney Island, Burro Series, and Maplin experiments with a tendency toward overprediction. There is a good agreement between the prediction of IIT Heavy Gas models I and II with those from DEGADIS, except for the simulations of IIT heavy gas model-I pertaining to very large release quantities under highly stable atmospheric conditions. In summary, the performance of IIT heavy gas models have been found to be reasonably good both with respect to the limited field data available and various simulations (selected on the basis of relevant storages in the industries and prevalent meteorological conditions performed with DEGADIS). However, there is a scope of improvement in the IIT heavy gas models (viz., better formulation for entrainment, modification of coefficients, transition criteria, etc.). Further, isotons (nomograms) have been prepared by using IIT heavy gas models for chlorine, which provide safe distance for various storage amounts for 24 meteorological scenarios prevalent in the entire year. These nomograms are prepared such that a nonspecialist can use them easily for control and management in case of an emergency requiring the evacuation of people in the affected region. These results can also be useful for siting and limiting the storage quantities.

Accidents, Occupational↗

scRNA-seq and bulk RNA-seq reveal the characteristics of macrophage copper metabolism and establish a risk signature in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent malignancy with an urgent need for improved prognostic stratification and treatment-response prediction. This study aimed to explore a macrophage copper metabolism-associated prognostic model and to investigate the relationship between this risk model and the tumor immune microenvironment. METHODS: The FindClusters function was used to analyze cell clusters, and CellChat and CellPhoneDB/LIANA were employed for cell-cell communication analysis. Copper metabolism-related genes were sourced from the MSigDB database. A prognostic risk model was established using least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression analysis, and a nomogram was constructed by integrating the prognostic model with clinicopathological factors. Additional analyses were performed to map the seven model genes in single-cell data, assess model uncertainty and robustness, evaluate macrophage/copper/cuproptosis-related transcriptional programs, and examine the correlations between risk score, immune infiltration and predicted drug sensitivity. RESULTS: Using single-cell RNA sequencing (scRNA-seq) data, we identified four macrophage subpopulations. Macrophages with high SPP1 expression showed close interaction with T cell populations and were associated with copper ion metabolism. By incorporating 141 copper metabolism-related genes and using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort, we constructed a seven-gene risk prediction model. Additional single-cell mapping showed that the model genes were detectable in the HCC single-cell dataset and showed a macrophage-associated expression pattern. The model showed moderate prognostic discrimination in TCGA-LIHC, whereas its external performance was heterogeneous and remained evaluable across external cohorts, with performance varying among datasets. Immune and mechanism-related analyses suggested that the risk signature was associated with macrophage-related infiltration, copper metabolism and cuproptosis-related transcriptional programs. Drug sensitivity analysis nominated Daporinad as a computationally predicted candidate compound, supporting Daporinad as a pharmacogenomic candidate for follow-up investigation. CONCLUSIONS: By integrating scRNA-seq and bulk RNA sequencing (RNA-seq) data, we constructed a macrophage copper metabolism-associated prognostic signature for HCC. The risk score was associated with survival, immune microenvironment features and predicted drug response, providing a transcriptomic framework for risk stratification and therapeutic hypothesis generation.

Hepatocellular carcinoma (HCC)↗

A prognostic model for functional outcome in early rheumatoid arthritis.

OBJECTIVE: To construct a prognostic algorithm to predict 5-year functional outcome in rheumatoid arthritis (RA), based on the Health Assessment Questionnaire (HAQ). METHODS: Data from all patients with 5-year followup (n = 985) were used from an inception cohort, the Early Rheumatoid Arthritis Study (ERAS). Possibly relevant prognostic factors considered in the initial stage of the model-building process were standard clinical, radiological, and laboratory features measured at baseline and at 1 year. Multivariate analysis was performed using logistic regression, and the predictive performance of the model was tested using measures of discrimination and calibration. RESULTS: Bootstrap resampling identified 6 variables that consistently predicted severe functional outcome. Functional grade III/IV (odds ratio 6.7) and HAQ at 1 year (odds ratio 2.4) were the most important. Other variables included socioeconomic status, hemoglobin, and radiographic and disease activity scores. Estimates of the regression coefficients and performance were corrected for over-fitting. Reasonably large values for the c-index (0.82) and the Nagelkerke R(2) (0.39) indicate that the set of prognostic factors explains the variation in outcome to a degree that implies good prediction for individual patients. CONCLUSION: The algorithm identifies patients in the first year of RA who are likely to have poor function by 5 years and who could potentially benefit from aggressive drug therapy. A nomogram is produced for simple application of the model in clinical practice. While further external validation is necessary, this model could allow clinicians to target aggressive therapy earlier in a patient's disease course.

Adult↗

Is biopsy Gleason score independently associated with biochemical progression following radical prostatectomy after adjusting for pathological Gleason score?

PURPOSE: Biopsy Gleason score is known to be associated with prostate specific antigen failure following radical prostatectomy. However, it is unclear whether it remains associated with outcome after surgery when the pathological Gleason score is known. MATERIALS AND METHODS: We determined the association between biopsy Gleason score and biochemical progression after correcting for preoperative and postoperative characteristics, including pathological Gleason score, in 1,931 men treated with radical prostatectomy between 1988 and 2005 in the Shared Equal Access Regional Cancer Hospital Database Study Group database. Gleason score was examined as a categorical variable of 2 to 6, 3 + 4 and 4 + 3 or greater. RESULTS: Higher biopsy Gleason scores were positively associated with extracapsular extension (p <0.001), positive surgical margins (p <0.001), seminal vesicle invasion (p <0.001), positive lymph nodes (p <0.001) and biochemical progression (log rank p <0.001). After adjusting for only preoperative characteristics biopsy Gleason 3 + 4 and 4 + 3 or greater were associated with increased risk of biochemical progression compared to biopsy Gleason 6 or less (p = 0.001 and <0.001, respectively). After further adjusting for multiple pathological characteristics, including pathological Gleason score, the association between higher biopsy Gleason score and progression was little changed, in that men with biopsy Gleason 3 + 4 and 4 + 3 or greater were significantly more likely to experience progression (p = 0.001 and <0.001, respectively). Furthermore, when stratified by pathological Gleason score, higher biopsy Gleason scores were associated with an increased risk of biochemical progression in each pathological Gleason score category (log rank p </=0.007). CONCLUSIONS: Biopsy Gleason score remained strongly associated with progression even when the pathological Gleason score was known and controlled for. If confirmed at other centers, incorporation of biopsy Gleason score into postoperative nomograms designed to estimate the progression risk might improve model precision.

Aged↗

Development and validation of a comprehensive prognostic model for 28-day ICU mortality in non-traumatic subarachnoid hemorrhage: an analysis based on the MIMIC-IV database.

BACKGROUND: Due to the complex pathophysiology of non-traumatic subarachnoid hemorrhage (SAH), accurate risk prediction remains a challenge. Our aim is to develop and validate a comprehensive prognostic model that integrates demographic characteristics, vital signs, laboratory parameters, and more, to provide clinical decision-making support in real-world practice. METHODS: We conducted a retrospective cohort study of 785 Non-traumatic subarachnoid hemorrhage patients. The cohort was randomly divided into a training set (n&#xa0;=&#xa0;549) and a validation set (n&#xa0;=&#xa0;236). Feature selection was performed using LASSO regression, followed by backward stepwise Cox regression for optimization. A nomogram was constructed based on independent predictive factors, and model performance was assessed using discrimination, calibration, and decision curve analysis. To prevent immortal-time bias, all predictors were anchored to a fixed early (first-24-hour) measurement window, treatment variables were modelled as binary indicators rather than cumulative exposures, and a five-model sensitivity analysis with baseline-severity adjustment was performed. RESULTS: The development of our model followed a systematic approach: first, 15 potential predictive factors were selected via LASSO regression, which were then refined to 12 independent predictors using backward stepwise Cox regression. The final predictive factors included: Ventilation, AHT, Nimodipine 60&#xa0;mg, Age, SAPS.II, Input amount, Calcium total, Platelet count, White blood cells, Anion gap, pH, and Chloride. The integrated model demonstrated excellent predictive ability for 7-day, 14-day, and 21-day mortality in both the training set (AUC: 0.972, 0.934, 0.898) and the validation set (AUC: 0.968, 0.948, 0.911). Calibration curves and decision curve analysis confirmed the model's reliability and clinical utility across different time points. We constructed a nomogram for individualized risk prediction. Univariate Kaplan-Meier survival analysis demonstrated significant stratification of survival outcomes by each predictor, while restricted cubic spline analysis revealed non-linear relationships between continuous variables and mortality risk. Random survival forest analysis identified the top three predictive factors (Nimodipine 60&#xa0;mg, Ventilation, AHT) and compared them with our full 12-variable model, confirming superior performance of the integrated model at all time points. At the 28-day primary endpoint, the model achieved a time-dependent AUC of 0.898 (training) and 0.904 (validation); after restricting predictors to the early baseline window, the leakage-controlled model retained good discrimination (validation C-index 0.803). CONCLUSIONS: Our ICU 28-day mortality prognosis model demonstrated robust performance in predicting ICU 28-day mortality in non-traumatic subarachnoid hemorrhage. The model, through the nomogram, provides individualized risk assessment, aiding clinical decision-making and patient stratification.

Humans↗

Predicting the risk of mobility difficulty in older women with screening nomograms: the Women's Health and Aging Study II.

BACKGROUND: A major obstacle to screening for early mobility disability (ie, mobility difficulty), a major public health concern, is the lack of a method that identifies those who are at high risk. The goal of this study was to develop easy-to-use clinical nomograms for estimation of the probability of incident mobility difficulty. METHODS: We conducted a population-based prospective study using data from 266 high physically and cognitively functioning older women, aged 70 to 80 years, who were free of mobility disability at the baseline evaluation of the Women's Health and Aging Study II. The outcome measure was incident mobility disability within 18 months, defined as self-reported difficulty walking 0.8 km, climbing 10 steps, or transferring from or into a car or bus. Logistic regression and receiver operating characteristic curve analyses were used for evaluation of the optimal combination of self-reported and performance-based mobility measures. Bootstrap sampling and estimation was used for validation. RESULTS: Predictive nomograms were developed based on a final model that included 3 simple-to-obtain measures of preclinical disability: self-report of modification in mobility tasks without having difficulty with them, one-leg stance balance, and time to walk 1 m at a usual pace. Final model accuracy (as estimated by the area under the receiver operating characteristic curve) was 73% (SE = 0.04). Validation analysis confirmed the high accuracy of these nomograms. CONCLUSIONS: An original tool was developed for assessment of the risk of mobility difficulty in older women that can be used to assist physicians and researchers in deciding which women to target for preventive interventions.

Aged↗

Validation of a nomogram for prediction of side specific extracapsular extension at radical prostatectomy.

PURPOSE: We have previously have reported a tree structured regression model for predicting SS-ECE. Others recently reported a logistic regression based SS-ECE nomogram. We developed a nomogram and compared the performance and discriminant properties of the tree regression and the nomogram in a contemporary cohort of European patients treated with radical retropubic prostatectomy. MATERIALS AND METHODS: The cohort consisted of 1,118 patients with pretreatment prostate specific antigen 0.1 to 73.2 ng/ml (median 6.6). Each of the 2,236 prostate lobes was considered separately. Clinical stage, pretreatment PSA, biopsy Gleason sum, percent positive cores and percent cancer in the biopsy specimen were used as predictors in a logistic regression model predicting SS-ECE. Regression coefficients were then used to generate an SS-ECE nomogram. Performance characteristics and discriminant properties of the previously published tree regression were also tested in the same cohort. For internal validation and to decrease overfit bias 200 bootstrap re-samples were applied to accuracy estimates for each method. RESULTS: ECE was present in 303 of 1,118 radical retropubic prostatectomy specimens (27%) and in 385 lobes (17%). In logistic regression models all variables were statistically significant multivariate predictors of SS-ECE except the percent of positive biopsy cores (p = 0.7). Bootstrap corrected predictive accuracy of the SS-ECE nomogram was 0.840 vs 0.700 for the tree regression model. CONCLUSIONS: Logistic regression based nomogram predictions of SS-ECE are highly accurate and represent a valuable aid for assessing the risk of ECE prior to surgery.

Calibration↗

Crucial role of telomere maintenance-related genes in survival prediction and subtype identification in colorectal cancer.

BACKGROUND: Telomere maintenance-related genes (TMRGs) are implicated in Colorectal cancer (CRC) development, but their prognostic value and clinical relevance remain insufficiently explored. This study aims to develop a TMRG-based prognostic model and elucidate its clinical utility in CRC management. METHODS: The Cancer Genome Atlas database was utilized to download RNA-seq data from 638 CRC and 51 control samples. Differential expressed genes were screened and intersected with 2086 TMRGs, resulting in the identification of 976 TMRGs. Through univariate and multivariate Cox regression analysis, a prognostic model comprising three telomere maintenance-related biomarkers (PDE1B, TFAP2B, and HSPA1A) was developed and validated using an external dataset. By integrating the model risk score with clinical features, a nomogram was constructed to predict the survival outcomes of CRC patients. Additionally, an in-depth investigation of the immuno-infiltration, functional variation and drug sensitivity analysis were performed in two risk subgroups defined by the prognostic model. Finally, the functional significance of PDE1B in CRC cell lines was investigated through MTT assays, cell colony formation assays, transwell assays and flow cytometry. RESULTS: A total of 976 DE-TMRGs were enriched in telomere/DNA replication pathways. A three-gene signature (PDE1B, TFAP2B, and HSPA1A) stratified patients into high- and low-risk groups with divergent survival (AUC >0.60, validated externally). High-risk patients had advanced N/M stages, elevated M0/M2 macrophages, reduced CD4+ memory T cells, and upregulated immune checkpoints. Nomogram integrating risk score, age, and N/M stage accurately predicted 1-/3-/5-year survival. Low-risk patients showed greater 5-fluorouracil sensitivity. PDE1B expression was significantly reduced in CRC tissues and correlated with advanced stages. Functional assays confirmed PDE1B overexpression suppressed proliferation, migration, invasion, and induced apoptosis in CRC cells. CONCLUSION: This study identifies a moderately predictive telomere maintenance-related gene signature as an independent prognostic predictor in CRC. The risk stratification model effectively discriminates patients with distinct survival patterns, tumor microenvironments, and therapeutic responses, while the integrated nomogram offers additional reference information for survival analysis, albeit with only moderate predictive accuracy. These findings indicate telomere maintenance-related gene signature could serve as a preliminary auxiliary risk stratification tool for postoperative CRC patients, PDE1B may also serve as a potential epithelial tumor-suppressor target for future preclinical studies.

PDE1B↗

Exercise versus recovery electrocardiography in predicting mortality in patients with uncomplicated myocardial infarction.

BACKGROUND: Exercise testing after acute myocardial infarction has limited prognostic accuracy. We prospectively used stress-recovery, heart rate-adjusted, ST-segment analysis to predict cardiac death in this clinical setting. METHODS: The stress-recovery index, defined as the difference in absolute values of the areas designated by ST depression in the heart-rate domain during exercise and recovery, was derived in 708 survivors of a first myocardial infarction. To assess whether it contributed additional prognostic information to routinely obtained information, clinical data, resting ejection fraction, and exercise testing data were entered into a sequential Cox model; the stress-recovery index was entered last. Model validation was performed by bootstrapping adjusted for the degree of optimism in estimates. Survival curves were set up using Kaplan-Meier analysis and compared by the log-rank test. RESULTS: Hypertension (OR 1.3, 95%CI 0.9-4.6), exercise capacity (OR 0.6, 95%CI 0.3-1.1 for the interquartile difference in kilopounds per minute), and the stress-recovery index (OR 0.7, 95%CI 0.5-0.9 for the interquartile difference) were independent predictors of cardiac death at a median follow-up of 32 months. However, the stress-recovery index enhanced the prognostic power of the model on top of clinical and exercise testing variables in all diagnostic subgroups according to ST-segment analysis and significantly discriminated survival. A simple nomogram was generated from the fitted Cox model to estimate risk in individual patients. CONCLUSIONS: Stress-recovery, heart rate-adjusted, ST-segment analysis predicts cardiac death after acute myocardial infarction and provides additional prognostic information over clinical and exercise testing data.

Cohort Studies↗

Development and external validation of an extended repeat biopsy nomogram.

PURPOSE: We hypothesized that the outcome of repeat biopsy could be accurately predicted. We tested this hypothesis in a contemporary cohort from 3 centers. MATERIALS AND METHODS: The principal cohort of 1,082 men from Hamburg, Germany was used for nomogram development as well as for internal 200 bootstrap validation in 721 and external validation in 361. Two additional external validation cohorts, including 87 men from Milan, Italy and 142 from Seattle, Washington, were also used. Predictors of prostate cancer on repeat biopsy were patient age, digital rectal examination, prostate specific antigen, percent free prostate specific antigen, number of previous negative biopsy sessions and sampling density. Multivariate logistic regression models were used to develop the nomograms. RESULTS: The mean number of previous negative biopsies was 1.5 (range 1 to 6) and the mean number of cores at final repeat biopsy was 11.1 (range 10 to 24). Of the men 370 (30.2%) had prostate cancer. On multivariate analyses all predictors were statistically significant (p < or =0.028). After internal validation the nomogram was 76% accurate. External validation showed 74% (Hamburg), 78% (Milan) and 68% (Seattle) accuracy. CONCLUSIONS: Relative to the previous nomograms (10 predictors or 71% accuracy) our tool relies on fewer variables (6) and shows superior accuracy in European men. Accuracy in American men is substantially lower. Racial, clinical and biochemical differences may explain the observed discrepancy in predictive accuracy.

Adult↗

Evaluation of renal functional parameters in different settings of isolated organ hemoperfusions.

Isolated porcine kidneys are commonly used to study physiological and pathophysiological aspects of renal homeostasis but standardized evaluation procedures of renal function in this model do not exist so far. A double-logarithmical nomogram is established for filtration and reabsorption functions in isolated and hemoperfused porcine kidneys using different perfusion settings. Model validity was demonstrated by the levels of urine flow and sodium excretion showing expected alteration levels of lowering in the ADH-group and increasing in the furosemide-group of isolated kidneys. Creatinine-clearance values were in constant ranges within each specific perfusion group as indicated by the nomogram procedure. The present studies used a nomogram method to analyze the effects of different renal perfusion settings in a porcine model of kidney perfusion. The method may be of use to differentiate various kidney perfusion parameters both at the experimental and clinical levels.

Animals↗

Subtype specific prognostic nomogram for patients with primary liposarcoma of the retroperitoneum, extremity, or trunk.

OBJECTIVE: To determine the prognostic significance of histologic subtype in a large series of patients with primary liposarcoma (LS) and to construct a LS-specific postoperative nomogram for disease-specific survival (DSS). SUMMARY BACKGROUND DATA: Nomograms, used to define and predict outcome following operative intervention, may contain variables not conventionally used in standard staging systems. A 12-year DSS postoperative nomogram for all sarcomas has already been established. METHODS: From a single-institution prospective sarcoma database, patients with primary extremity, truncal, or retroperitoneal LS treated between 1982 and 2005 were identified. Histology was reviewed by a sarcoma pathologist and divided into 5 subtypes. A nomogram predictive of 5- and 12-year DSS was developed. RESULTS: Of 801 patients with primary LS resected with curative intent, 369 (46%) presented with well-differentiated, 143 (18%) dedifferentiated, 144 (18%) myxoid, 81 (10%) round cell, and 64 (8%) pleomorphic histology. The median tumor burden was 15 cm (range, 1-139 cm). At last follow-up, 560 patients were alive with a median follow-up time of 45 months (range, 1-264 months) and 51 months for surviving patients. The 5- and 12-year DSS rates were 83% (95% confidence interval [CI], 80%-86%) and 72% (95% CI, 67%-77%), respectively. The nomogram was drawn on the basis of a Cox regression model. The independent predictors of DSS were age, presentation status, histologic variant, primary site, tumor burden, and gross margin status. The nomogram was internally validated using bootstrapping and shown to have excellent calibration. The concordance index was 0.827 compared with 0.776 for the general sarcoma postoperative nomogram for 12-year DSS. CONCLUSION: The LS-specific nomogram based on histologic subtype provides more accurate survival predictions for patients with primary LS than the previously established generic sarcoma nomogram. DSS nomograms aid in more accurate counseling of patients, identification of patients appropriate for adjuvant therapy, and stratification of patients for clinical trials and molecular analysis.

Adolescent↗

A regional blood circulation alternative to in-series two compartment urea kinetic modeling.

Assuming that the clearance of urea from total body water (TBW) is flow limited, the authors developed a parallel flow model using physiologic data. Organ systems with a blood flow to water volume ratio of greater than 0.2 min-1 were allocated to the high flow system. Remaining organs were represented in the low flow system. In end-stage renal disease patients with minimal renal blood flow, the high flow system contained 20% TBW and received 70% of the systemic blood flow. The authors used this flow heterogeneity to predict the post-dialysis urea rebound (R) in 12 patients after 1 hr of hemodialysis. Dialyzer clearance was 248 +/- 14.5 ml/min (mean +/- SEM) Access recirculation was obviated by returning cleared blood into a central vein. In these patients, R at 1, 3, 5, 7, 10, and 15 minutes. after slowing dialyzer blood flow (Qb) from 383 +/- 18 to 50 ml/min was 3.8 +/- 2.9, 6.2 +/- 3.4, 7.6 +/- 3.1, 8.8 +/- 3.9, 9.0 +/- 4.1, and 9.9 +/- 4.4%, respectively. CO and QAc were modeled with values of 5.5 and 0.5 L/min, respectively. The modeled TBW was 35 L. Total body water derived by nomogram was 38.1 +/- 2.0 L. Our results suggest that the parallel-flow model for urea transport can be used to explain the amount and time course of post dialysis R on a physiologic basis.

Blood Flow Velocity↗

Development and validation of a nomogram predicting the outcome of prostate biopsy based on patient age, digital rectal examination and serum prostate specific antigen.

PURPOSE: We developed and validated a nomogram which predicts presence of prostate cancer (PCa) on needle biopsy. MATERIALS AND METHODS: We used 3 cohorts of men who were evaluated with sextant biopsy of the prostate and whose presenting prostate specific antigen (PSA) was not greater than 50 ng/ml. Data from 4,193 men from Montreal, Canada were used to develop a nomogram based on age, digital rectal examination (DRE) and serum PSA. External validation was performed on 1,762 men from Hamburg, Germany. Data from these men were subsequently used to develop a second nomogram in which percent free PSA (%fPSA) was added as a predictor. External validation was performed using 514 men from Montreal. Both nomograms were based on multivariate logistic regression models. Predictive accuracy was evaluated with areas under the receiver operating characteristic curve and graphically with loess smoothing plots. RESULTS: PCa was detected in 1,477 (35.2%) men from Montreal, 739 (41.9%) men from Hamburg and 189 (36.8%) men from Montreal. In all models all predictors were significant at 0.05. Using age, DRE and PSA external validation AUC was 0.69. Using age, DRE, PSA and %fPSA external validation AUC was 0.77. CONCLUSIONS: A nomogram based on age, DRE, PSA and %fPSA can highly accurately predict the outcome of prostate biopsy in men at risk for PCa.

Adolescent↗

Population pharmacokinetics of gentamicin in patients with cancer.

AIMS: The purpose of this study was to describe the population pharmacokinetics of gentamicin in patients with cancer, to identify possible relationships between clinical covariates and population pharmacokinetic parameter estimates and to examine the relevance of existing dosage nomograms in light of the population model developed in these patients. METHODS: Data were collected prospectively from 210 patients with cancer and were analysed with package NONMEM. Data were split into two sets: a population data set and an evaluation set. Creatinine clearance was estimated using measured creatinine concentrations and using 'low' creatinines set to a minimum of 60 micromol l(-1), 70 micromol l(-1) or 88.4 micromol l(-1) RESULTS: A two compartment model was fitted to the concentration-time curve. Two best models were obtained, one that related clearance to estimated creatinine clearance (minimum creatinine value 60 micromol l(-1)) and the other that related clearance to age, creatinine concentration and body surface area. Volume of the central compartment was influenced by body surface area and albumin concentration. For both models 90% of measured concentrations lay within the 95% confidence interval of the simulated concentrations and the mean prediction errors were -7.2% and -6.6%, respectively. A final analysis performed in all patients identified the following relationship CL (1 h(-1))=0.88 x (1 + 0.043 x creatinine clearance) and central volume of distribution V1 (1)=8.59 x body surface area x (albumin/34)(-0.39). The mean population estimate of intercompartmental clearance (Q) was 1.301 h(-1) and peripheral volume of distribution (V2) was 9.801. Coefficient of variation was 18.5% on clearance and 28.2% on Q. Residual error expressed as a standard deviation was 0.36 mg l(-1) at 1.0 mg l(-1) and 1.32 mg l(-1) at 8.0 mg l(-1). The mean population estimate of clearance was 4.21 h(-1) and volume of distribution (Vss) was 24.61 (0.381 kg(-1)). The mean population estimates of half-lives were 1.8 h and 8.0 h. CONCLUSIONS: In the context of published nomograms this analysis indicated that both the traditional approach and the new, 'once daily' approach should achieve satisfactory concentrations in cancer patients although serum concentration monitoring is required to confirm optimal dosing in individual patients.

Adolescent↗

Maximal oxygen consumption in patients with lung disease.

A theoretical model for oxygen transport assuming a series linkage of ventilation, diffusion, oxygen uptake by erythrocytes, cardiac output, and oxygen release was used to calculate expected values for maximal oxygen intake (VO2max) of patients with various pulmonary disorders 22 patients with either restrictive or obstructive ventilatory impairment were studied at rest and maximal exercise. When exercise measurements of maximal pulmonary blood flow (QCmax), oxygen capacity, membrane diffusing capacity for CO, pulmonary capillary blood volume, alveolar ventilation, and mixed venous oxygen saturation were employed as input values, predictions of VO2max from the model correlated closely with measured values (r = 0.978). Measured VO2max was 976+/-389 ml/min (45.3+/-13% of predicted normal), and VO2max predicted from the model was 1,111+/-427 ml/min. The discrepancy may in part reflect uneven matching of alveolar ventilation, pulmonary capillary blood flow, and membrane diffusing capacity for CO within the lung; uniform matching is assumed in the model so that mismatching will impair gas exchange beyond our predictions. Although QCmax was less than predicted in most patients (63.6+/-19.6% of predicted) the model suggests that raising QCmax to normal could have raised VO2max only 11.6+/-8.8% in the face of existent impairment of intrapulmonary gas exchange. Since pulmonary functions measured at rest correlated well with exercise parameters needed in the model to predict VO2max we developed a nomogram for predicting VO2max from resting CO diffusing capacity, the forced one second expired volume, and the resting ratio of dead space to tidal volume. The correlation coefficient between measured and predicted VO2max, by using this nomogram, was 0.942.

Adolescent↗

Identifying newborns at risk of significant hyperbilirubinaemia: a comparison of two recommended approaches.

AIMS: To compare the predictive performance of clinical risk factor assessment and pre-discharge bilirubin measurement as screening tools for identifying infants at risk of developing significant neonatal hyperbilirubinaemia (post-discharge total serum bilirubin (TSB) >95th centile). METHODS: Retrospective cohort study of term and near term infants born in an urban community teaching hospital in Pennsylvania (1993-97). A clinical risk factor scoring system was developed and its predictive performance compared to a pre-discharge TSB expressed as a risk zone on a bilirubin nomogram. Main outcome measures were prediction model discrimination, range of predicted probabilities, and sensitivity, specificity, positive and negative predictive values, and likelihood ratios for various positivity criteria. RESULTS: The clinical risk factor scoring system developed included birth weight, gestational age <38 weeks, oxytocin use during delivery, vacuum extraction, breast feeding, and combination breast and bottle feeding. The pre-discharge bilirubin risk zone had better discrimination (c = 0.83; 95% CI 0.80 to 0.86) than the clinical risk factor score (c = 0.71; 95% CI 0.66 to 0.76) and predicted risk of significant hyperbilirubinaemia as high as 59% compared with a maximum of 44% for the clinical risk factor score. Neither the risk score nor the pre-discharge TSB risk zone predicted the outcome with > or =0.98 sensitivity without significantly compromising specificity (0.13 and 0.21, respectively). Multi-level clinical risk factor scores and TSB risk zones produced likelihood ratios of 0.15-3.25 and 0.05-9.43, respectively. CONCLUSIONS: The pre-discharge bilirubin expressed as a risk zone on an hour specific bilirubin nomogram is more accurate and generates wider risk stratification than a clinical risk factor score.

Cohort Studies↗

Predicting the presence and side of extracapsular extension: a nomogram for staging prostate cancer.

PURPOSE: We developed a model to predict the side specific probability of extracapsular extension (ECE) in radical prostatectomy (RP) specimens based on the clinical features of the cancer. MATERIALS AND METHODS: We studied 763 patients with clinical stage T1c-T3 prostate cancer who were diagnosed by systematic needle biopsy and subsequently treated with RP. Candidate predictor variables associated with ECE were clinical T stage, the highest Gleason sum in any core, percent positive cores, percent cancer in the cores from each side and serum prostate specific antigen (PSA). Receiver operating characteristic (ROC) analyses were performed to assess the predictive value of each variable alone and in combination. We constructed and internally validated nomograms to predict the side specific probability of ECE based on logistic regression analysis. RESULTS: Overall 30% of the patients and 17% of 1,526 prostate lobes (left or right) had ECE. The areas under the ROC curves (AUC) of the standard features in predicting side specific probability of ECE were 0.627 for PSA, 0.695 for clinical T stage on each side and 0.727 for Gleason sum on each side. When these features were combined predictive accuracy increased to 0.788. The highest value (0.806) was achieved by adding the percent positive cores and the percent cancer in the biopsy specimen to the standard features. The resulting nomograms were internally validated and had excellent calibration and discrimination accuracy. CONCLUSIONS: Standard clinical features of prostate cancer in each lobe-PSA, palpable induration and biopsy Gleason sum-can be used to predict the side specific probability of ECE in RP specimens. The predictive accuracy is increased by adding information from systematic biopsy results. The predictive nomograms are sufficiently accurate for use in clinical practice in decisions such as wide versus close dissection of the cavernous nerves from the prostate.

Adult↗