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PubMed1996

Use of in vitro assays to predict the efficacy of chemopreventive agents in whole animals.

Five in vitro assays have been applied to screen the efficacy of potential chemopreventive agents. These assays measure a) inhibition of morphological transformation in rat tracheal epithelial (RTE) cells, b) inhibition of anchorage independence in human lung tumor (A427) cells, c) inhibition of hyperplastic alveolar nodule formation in mouse mammary organ cultures (MMOC), d) inhibition of anchorage independence in mouse JB6 epidermal cells, and e) the inhibition of calcium tolerance in human foreskin epithelial cells. The efficacy of many of these same agents in whole animal studies of lung, colon, mammary gland, skin, and urinary bladder carcinogenesis has also been measured. The aim herein is to estimate the positive and negative predictive values of these in vitro assays against whole animal chemopreventive efficacy data using the same chemicals. For three of these assays--using RTE, A427 cells and mouse mammary organ culture (MMOC)-enough data are available to allow the estimate to be made. Such extrapolations of in vitro data to the in vivo situation are difficult at best. There are many dissimilarities between the two assay systems. The in vitro assays use respiratory and mammary epithelial cells, while the in vivo assays use respiratory, mammary, colon, bladder and skin cells. The in vitro assays use the carcinogens benzo(a)pyrene (B(a)P) and 7,12-dimethylbenz(a)anthracene (DMBA), while the in vivo assays use B(a)P, DMBA, N-methyl-N-nitrosourea (MNU), N,N'-diethylnitrosamine (DEN), azoxymethane (AOM), and N-butyl-N-(4-hydroxybutyl)nitrosoamine (OH-BBN). There are vast differences in pharmacodynamics and pharmacokinetics in vitro and in vivo, yet it is possible to rapidly screen chemicals in vitro for efficacy at one-tenth the cost and complete tests in weeks instead of months. A positive in vitro assay was defined as a 20% inhibition (compared with control) for the RTE and A427 assays and a 60% inhibition for the MMOC assay at nontoxic concentrations. For in vivo assays, the criterion for a positive result was a statistically significant inhibition of incidence, multiplicity or a significant increase in latency (mean time to first tumor). For an agent to be considered negative in animals, it required negative results in at least two different organ systems and no positive results. Using the battery of three in vitro tests, the positive predictive value for having one, two, or three positive in vitro assays and at least one positive whole animal test was 76%, 80%, and 83% respectively. The negative predictive values for one, two or all three in vitro assays was 25%, 27%, and 50%. From these data it is observed that in vitro assays give valuable positive predictive values and less valuable negative predictive values. The mechanisms of chemoprevention are not well understood. Seven categories of agents were examined for their cancer preventing both in vitro and in vivo: antiinflammatories, antioxidants, arachadonic acid metabolism inhibitors, GSH inducers, GST inducers, ODC inhibitors, and PKC inhibitors. Three or even five in vitro assays cannot be all-inclusive of the many mechanisms of cancer prevention. However, three assays help to predict whole animal efficacy with reasonable positive predictive values. Much work and development remains to be done to rapidly identify new chemopreventive drugs.

Animals↗
PubMed2003

Prediction of skin penetration using artificial neural network (ANN) modeling.

Artificial neural network (ANN) analysis was used to predict the skin permeability of selected xenobiotics. Permeability coefficients (log k(p)) were obtained from various literature sources. A previously reported equation, which was shown to be useful in the prediction of skin permeability, uses the partial charges of the penetrants, their molecular weight, and their calculated octanol water partition coefficient (log K(oct)). The equation was used to predict the skin permeability for the set of 40 compounds (r(2) = 0.672). A successful ANN was developed and the ANN produced log k(p) values that correlated well with the experimental ones(r(2) = 0.997). The penetration properties of a selection of compounds through human skin that have not been previously investigated, etodolac, famotidine, nimesulide, nizatidine, ranitidine, were investigated. Their permeability coefficients were determined. It was then possible to compare the experimental data with that predicted using the partial charge equation and the trained ANN. ANN modeling for predicting skin permeability was found to be useful for predicting skin permeability coefficients of compounds. In conclusion, the developed and described ANN model in this publication does not require any experimental parameters; it could potentially provide useful and precise prediction of skin penetration for new drugs or toxic penetrants.

Artificial Intelligence↗
PubMed2004

Interspecies scaling of protein drugs: prediction of clearance from animals to humans.

The objective of this study was to evaluate the interspecies scaling for therapeutic protein drugs to predict the systemic clearance in humans from animal data. This study was undertaken to examine whether the rule of exponents of Mahmood and Balian for the prediction of clearance of nonprotein drugs can also be extended to macromolecules. Three different methods were used to predict clearance in humans: (1). clearance versus body weight (simple allometry), (2). the product of clearance and maximum life-span potential (MLP) versus body weight, and (3). the product of clearance and brain weight versus body weight. Data from 15 therapeutic proteins were analyzed, and the results indicated that the clearance of protein drugs can be predicted accurately using simple allometry or brain weight depending on the exponents of the simple allometry. Furthermore, these 15 drugs were scaled up using two species and the predicted clearance was compared with the predicted clearance obtained from more than two species. It was found that more than two species are needed for a reliable prediction of clearance.

Animals↗
PubMed2004

Rapid and accurate prediction of degradant formation rates in pharmaceutical formulations using high-performance liquid chromatography-mass spectrometry.

Rapid and accurate stability prediction is essential to pharmaceutical formulation development. Commonly used stability prediction methods include monitoring parent drug loss at intended storage conditions or initial rate determination of degradants under accelerated conditions. Monitoring parent drug loss at the intended storage condition does not provide a rapid and accurate stability assessment because often <0.5% drug loss is all that can be observed in a realistic time frame, while the accelerated initial rate method in conjunction with extrapolation of rate constants using the Arrhenius or Eyring equations often introduces large errors in shelf-life prediction. In this study, the shelf life prediction of a model pharmaceutical preparation utilizing sensitive high-performance liquid chromatography-mass spectrometry (LC/MS) to directly quantitate degradant formation rates at the intended storage condition is proposed. This method was compared to traditional shelf life prediction approaches in terms of time required to predict shelf life and associated error in shelf life estimation. Results demonstrated that the proposed LC/MS method using initial rates analysis provided significantly improved confidence intervals for the predicted shelf life and required less overall time and effort to obtain the stability estimation compared to the other methods evaluated.

Algorithms↗
PubMed2004

The value of ultrasound in the prediction of successful induction of labor.

OBJECTIVES: To examine the value of pre-induction sonographic assessment of cervical length, posterior cervical angle and occipital position in the prediction of the induction-to-delivery interval within 24 h, the likelihood of vaginal delivery within 24 h, the likelihood of Cesarean section and to compare sonographic assessment with the Bishop score. METHODS: In 604 singleton pregnancies, induction of labor was carried out at 35-42 weeks of gestation. Immediately before induction, transvaginal sonography was performed for measurement of cervical length and posterior cervical angle and a transabdominal scan was carried out to determine the position of the fetal occiput. The value of occipital position, posterior cervical angle, cervical length, parity, gestational age, maternal age, and body mass index (BMI) on the induction-to-delivery interval within 24 h, the likelihood of vaginal delivery within 24 h and the likelihood of Cesarean section were investigated by Cox proportional hazard model or logistic regression analysis. RESULTS: Vaginal delivery occurred in 484 (80.1%) women and this was within 24 h of induction in 388 (64.2%). Cesarean section was performed in 120 (19.9%). Occiput-anterior (OA) and transverse (OT) positions were analyzed as one group as the odds ratios (OR) and the HR were similar and different from occiput-posterior (OP), which was analyzed as another group. Prediction of the induction-to-delivery interval was provided by the occipital position, pre-induction cervical length, parity and posterior cervical angle. Prediction of the likelihood of vaginal delivery within 24 h was provided by the occipital position, cervical length, posterior cervical angle and BMI. Prediction of the likelihood of Cesarean section was provided by the occipital position, cervical length, parity, maternal age and BMI. In the prediction of vaginal delivery within 24 h, for a specificity of 75%, the sensitivity for ultrasound findings was 89% and for the Bishop score it was 65%. The respective sensitivities for Cesarean section were 78% and 53%. CONCLUSION: In women undergoing induction of labor, significant independent prediction of the induction-to-delivery interval within 24 h, the likelihood of vaginal delivery within 24 h and the likelihood of Cesarean section are provided by pre-induction cervical length, occipital position, posterior cervical angle and maternal characteristics. Sonographic parameters were superior to the Bishop score in the prediction of the outcome of induction.

Body Mass Index↗
PubMed2001

Predictive value of the ATP chemosensitivity assay in epithelial ovarian cancer.

OBJECTIVE: The aims of this study were to define the specific parameters of the ATP chemosensitivity assay which most accurately predict a patient's clinical response to chemotherapeutic agents in epithelial ovarian cancer and to assess the clinical utility of the ATP assay. METHODS: In our laboratory from 1992 to 1994, fresh tumor specimens from patients with epithelial ovarian carcinomas were assayed with the ATP chemosensitivity assay (ATP-CSA) for their in vitro responses to several chemotherapeutic agents including cisplatin, paclitaxel, and cyclophosphamide. Clinical data on 161 of those patients including all follow-up assessments were then collected, and an investigator blinded to the in vitro assay results determined the patients' responses to chemotherapy. In order to determine which parameter of the assay was the best predictor of clinical response for each drug, receiver-operator characteristic (ROC) curves were constructed for several parameters, including the amount of cell kill at particular dosage levels of drug, the slope of the dose-response curve, and the IC50, or the average concentration of drug at which 50% of the cells were nonviable. RESULTS: The specific parameter of the ATP-CSA which was most predictive of clinical response differed for each drug tested. The resulting positive predictive values for cisplatin, cyclophosphamide, and paclitaxel ranged from 70.0 to 78.3% and negative predictive values from 46.2 to 60.9%, with overall ATP-CSA positive and negative predictive values of 83.0 and 56.5%. Overall, patients whose tumors tested sensitive to an agent in vitro were almost twice as likely (83% versus 43%) to show a clinical response (RR 1.91, 95% CI 1.34-2.71). CONCLUSION: Analysis of the ROC curves in this study shows that different parameters of the ATP-CSA need to be utilized for each drug tested in order to give the best prediction of clinical chemosensitivity. Although the ATP-CSA shows predictive ability, routine use of the ATP-CSA for clinical selection of drug therapy in patients with epithelial ovarian cancer would not be warranted without a prospective study comparing chemotherapy treatment based on assay results versus clinician selection of drug.

Adenosine Triphosphate↗
PubMedDate not supplied

Prediction of blood cyclosporine concentrations in haematological patients with multidrug resistance by one-, two- and three-compartment models using Bayesian and non-linear least squares methods.

The blood cyclosporine (CsA) concentration-time profile in each of 24 adult haematological patients with multidrug resistance taking the first course of CsA treatment was fitted by one-, two- and three-compartment models to obtain relevant pharmacokinetic parameters. The pharmacokinetic parameters obtained were implemented into the PKS program (Abbottbase Pharmacokinetic System) as the population pharmacokinetic parameters used to predict blood CsA concentrations in adult haematological patients with multidrug resistance. The predictions of blood CsA concentrations by one-, two- and three-compartment models using the Bayesian method (BM) and the non-linear least squares method (NLLSM) were evaluated employing 11 patients who took the second course of CsA treatment. While the Akaike's information criterion (AIC) favoured the two-compartment model to describe CsA concentration-time profiles in patients taking the first and second courses of CsA treatment, the predictive performance analyses showed that both two- and three-compartment models were better than the one-compartment model for prediction, but the three-compartment model was slightly superior to the two-compartment model. The results also show that the predictions using BM were slightly better than those using NLLSM. Several factors affecting BM predictions and the possible difference among AIC, BM and predictive performance analyses were also addressed.

Acute Disease↗
PubMed1996

Use of in vitro skin penetration data and a physiologically based model to predict in vivo blood levels of benzoic acid.

A physiologically based pharmacokinetic (PB PK) model was developed to predict plasma levels of benzoic acid (BA) in the hairless guinea pig after topical exposure at three finite dose levels. The PB PK model consisted of four compartments: (1) rapidly perfused tissues; (2) slowly perfused tissues; (3) liver, representing the route of elimination of BA from the plasma after biotransformation to hippuric acid; and (4) plasma. The predictive capacity of the PB PK model was assessed by comparing plasma BA levels measured experimentally with those predicted by the model. The percutaneous absorption of finite doses of BA in the model was described by a transdermal input function, which was derived from in vitro percutaneous absorption studies in which viable hairless guinea pig skin in flow-through diffusion cells was exposed to BA. Physiological parameters used in the model were calculated from previously published values. Biochemical parameters, including partition coefficients and metabolic constants, were measured experimentally in vitro. The PB PK model predictions were generally in good agreement with measured plasma levels for each of the dose levels studied. The predicted plasma BA levels and the measured values were closer for the highest dose (120 microg/cm2) than for either of the other two doses used (12 and 40 microg/cm2). The effects of optimizing the metabolic constants and the transdermal input function parameters on the predicted curve shape and fit to that of the measured plasma BA levels were assessed. Varying the transdermal input parameters produced closer agreement between predicted and measured values.

Administration, Topical↗
PubMed1991

Predicting outcome in intensive therapy units--a comparison of Apache II with subjective assessments.

In a prospective study 568 patients admitted to a mixed medical and surgical intensive therapy unit (ITU) were assessed using the Apache II severity of illness score to predict outcome. Their outcome was also predicted subjectively by a doctor and nurse on admission. There were 260 deaths in the group. The subjective predictions were compared with the Apache II predictions using logistic regression analysis and receiver-operating-characteristic curve measurement. The subjective assessments were found to be a more powerful predictor of outcome in this group of patients than the Apache II scores and predicted risk of death. Although the predictions could be successfully applied to the population as a whole, none of the tests were suitable for predicting outcome on an individual patient.

Decision Making↗
PubMed1994

Contribution of non-neurologic disturbances in acute physiology to the prediction of intensive care outcome after head injury or non-traumatic intracranial haemorrhage.

OBJECTIVE: To study the additional contribution of non-neurologic disturbances in acute physiology and chronic health to the prediction of intensive care outcome in patients with head injury or non-traumatic intracranial haemorrhage. DESIGN: A nationwide study in Finland with prospectively collected data on all adult patients admitted to intensive care after head trauma or non-traumatic intracranial haemorrhage during a 14-month period. Two-thirds of the patients were randomly selected to derive predictive models, and the remaining one third constituted the validation sample. SETTING: A total of 25 medical and surgical ICUs in Finland (13 in tertiary referral centers). PATIENTS: 901 consecutive adult patients with head injury or non-traumatic intracranial haemorrhage. MEASUREMENTS AND RESULTS: Variables of the APACHE II including Glasgow Coma Score were collected at the time of ICU admission. Two predictive models were created to explain hospital mortality. The addition of variables describing acute physiology to a predictive model consisting of Glasgow Coma Score, age, diagnosis of head injury and the type of ICU admission did not increase its performance in discriminating between survivors and nonsurvivors, but the calibration accuracy of the predictive model especially at the high ranges of risk was improved. CONCLUSIONS: The non-neurologic disturbances in acute physiology have prognostic significance in the prediction of intensive care outcome in patients with head injury or non-traumatic intracerebral haemorrhage. The created predictive model may supplement clinical judgement of this patient group.

APACHE↗
PubMed2001

The predictive value of plasma fibronectin concentration on fetal growth retardation at earlier stage of the third trimester.

In order to evaluate the predictive value of maternal plasma fibronectin (FN) concentration at 24-34 weeks on fetal intrauterine growth retardation (IUGR), a prospective double-blinded study was performed. The maternal plasma FN concentrations were measured by using a rate nephelometric procedure in the 130 initial normal nulliparous pregnant woman at 24-34 gestational weeks. The outcome of pregnancies and birth weight of their infants were followed up. IUGR was defined as that the birth weight was less than the 10th percentile for gestational age. The receiver operating characteristic curves and predictive values of FN predicting on outcome of pregnancy with IUGR were analyzed. The results showed that: (1) In a cohort of 130 initially normal nulliparous pregnant women, IUGR occurred in 14 cases during the follow-up; (2) The plasma FN levels in the women with IUGR (467.58 +/- 104.43 mg/L) were significantly higher than in the normal control group (299.44 +/- 105.55 mg/L, P < 0.01). However, there was no significant difference in the mean maternal age, gravidity, sampling gestational ages, delivering gestational ages between the two groups (P > 0.05); (3) The areas under ROC curve for predicting the outcome of pregnancy in IUGR was 0.893; (4) At the cut point of 475 mg/L FN level, the sensitivity, specificity, positive predictive value, negative predictive value and Kappa index for predicting the outcomes of pregnancy in IUGR were 57.14%, 95.69%, 61.54%, 94.87%, 0.5455 respectively. It was concluded that the maternal plasma FN might be used as an earlier predictor for screening of IUGR.

Adult↗
PubMed2004

Limitations of score-based daily outcome predictions in the individual intensive care patient. An example of the RIAHDH algorithm.

OBJECTIVE: The nature of score-based predictions is probabilistic, and their accuracy depends on the reliability and validity of the applied system. As an example, the present study investigates the accuracy of the RIP-algorithm (RIP = Riyadh Intensive Care Program) based on daily APACHE II scores, and compares it with published results of that algorithm from other investigators. DESIGN: Prospective observational study and review of the literature. PATIENTS AND METHODS: 1,986 consecutive admissions of 1,808 patients to a surgical intensive care unit were documented. Daily changes of score values were used to derive a risk of death estimation. Sensitivity and the rate of false predictions were calculated for score-based predictions. Health status one year after discharge was assessed in survivors predicted to die. RESULTS: Daily application of the algorithm identified 109 situations leading to death predictions in 56 patients. Five of these patients were discharged alive from the hospital (positive predictive value 91%). One year later 3 of these patients were still alive. The algorithm identified 51 of the non-survivors (sensitivity 19%), 110 died in the ICU without prediction. Altogether 270 patients died during their hospital stay. Among the 6 independent validation studies, similar results were found, but differences occurred due to the problematic assessment of consciousness. CONCLUSIONS: Sequential assessment of scores in intensive care could identify high risk patients, but with some degree of uncertainty. Therefore, the scores should only be used by those familiar with their limitations and risks.

APACHE↗
PubMed2001

Statistical models to predict the need for postoperative intensive care and hospitalization in pediatric surgical patients.

OBJECTIVE: To develop statistical models for predicting postoperative hospital and ICU stay in pediatric surgical patients based on preoperative clinical characteristics and operative factors related to the degree of surgical stress. We hypothesized that preoperative and operative factors will predict the need for ICU admission and may be used to forecast the length of ICU stay or postoperative hospital stay. DESIGN: Prospective data collection from 1,763 patients. SETTING: Tertiary care children's hospital. PATIENTS AND PARTICIPANTS: All pediatric surgical patients, including those undergoing day surgery. Patients undergoing dental or ophthalmologic surgical procedures were excluded. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: A logistic regression model predicting ICU admission was developed from all patients. Poissonregression models were developed from 1,161 randomly selected patients and validated from the remaining 602 patients. The logistic regression model for ICU admission was highlypredictive (area under the receiver operating characteristics (ROC) curve = 0.981). In the data set used for development of Poisson regression models, significant correlations occurred between the observed and predicted ICU stay (Pearson r = 0.468, p < 0.0001, n = 131) and between the observed and predicted hospital stay for patients undergoing general (r = 0.695, p < 0.0001), orthopedic (r = 0.717, p < 0.0001), cardiothoracic (r = 0.746, p < 0.0001), urologic (r = 0.458, p < 0.0001), otorhinolaryngologic (r = 0.962, p < 0.0001), neurosurgical (r = 0.7084, p < 0.0001) and plastic surgical (r = 0.854, p < 0.0001) procedures. In the validation data set, correlations between predicted and observed hospital stay were significant for general (p < 0.0001), orthopedic (p < 0.0001), cardiothoracic (p = 0.0321) and urologic surgery (p = 0.0383). The Poisson models for length of ICU stay, otorhinolaryngology, neurosurgery or plastic surgery could not be validated because of small numbers of patients. CONCLUSIONS: Preoperative and operative factors may be used to develop statistical models predicting the need for ICU admission in pediatric surgical patients, and hospital stay following general surgical, orthopedic, cardiothoracic and urologic procedures. These statistical models need to be refined and validatedfurther, perhaps using data collection from multiple institutions.

Arkansas↗
PubMed2003

Superiority of age and weight as variables in predicting osteoporosis in postmenopausal white women.

UNLABELLED: Identification of women at risk for osteoporosis is of great importance for the prevention of osteoporotic fractures. Routine BMD measurement of all women is not feasible for most populations, hence identification of a high-risk subset of women is an important element of effective preventive strategies. METHODS: We identified 959 postmenopausal non-Hispanic women aged 51 years and above from the NHANES III study to assess the relative contribution of risk predictors for low BMD at the whole proximal femur and the femoral neck regions. Based on recognized risk factors for osteoporosis identified by a systematic literature search, we ran several multiple linear regression models based on the results of preceding bivariate analyses. We show several models based on their explanatory ability assessed by adjusted r(2), ROC, and C-value analyses rather than on the coefficients and P values. We furthermore examined the sensitivity, specificity, and predictive values of our preferred models for various cutoff T-scores-the choice of which will vary depending on different study goals and population characteristics. RESULTS: Age and weight were by far the most informative predictors for low bone mineral density out of a list of 20 candidate risk predictors. Our preferred prediction models for the two regions hence contained only two variables: i.e., age and measured weight. The resulting parsimonious model to predict BMD at whole proximal femur had an adjusted r(2) of 0.43, an area under the ROC curve of 0.85, and a C-value of 0.70. Similarly, prediction for BMD at the femoral neck had adjusted r(2), area under the curve, and C-value of 0.39, 0.83, and 0.66, respectively. CONCLUSIONS: The model equations, predicted T-score = -1.332-0.0404 x (age) + 0.0386 x (measured weight) and predicted T-score = -1.318-0.0360 x (age) + 0.0314 x (measured weight) for whole proximal femur and femoral neck, respectively, can be used in field conditions for screening purposes. More complex prediction equations add little explanatory power. Based on the study goals and the population characteristics, specific cutoff T-scores have to be decided before using these equations.

Age Factors↗
PubMed1999

Prediction of aminoglycoside distribution space in neonates by multiple frequency bioelectrical impedance analysis.

OBJECTIVE: Aminoglycoside antibiotics have a narrow margin of safety between therapeutic and toxic levels. The current study used multiple frequency bioelectrical impedance analysis to develop prediction equations for gentamicin distribution space in neonates. METHODS: Gentamicin pharmacokinetic parameters and bioimpedance were measured in 14 infants in the neonatal intensive care unit. Stepwise regression analysis was used to develop predictive models, using impedance quotients (F2/R), weight and gestational age as variables, whose predictive performance was then tested in a second group of ten infants. RESULTS: The prediction model with the smallest bias and highest concordance correlation was that which included F2/R0 and weight. This bias of 50 ml or 6.7% was less than half of that found using a model including weight alone. CONCLUSION: A bioelectrical impedance-based prediction equation for prediction of gentamicin distribution space in neonates was produced. Although this prediction equation represents only a small improvement over that using weight alone, this is of clinical significance due to the narrow margin between therapeutic and toxic levels for gentamicin. A clinical trial to confirm the value of this methodology is now warranted.

Anti-Bacterial Agents↗
PubMedDate not supplied

Noninvasive prediction of pulmonary artery pressure in patients with isolated ventricular septal defect.

Management of patients with isolated ventricular septal defect (VSD) requires information regarding pulmonary artery pressure (PAP). The purpose of this study was to evaluate the individual predictive value of noninvasive methods for assessment of PAP and to determine if any combination of techniques significantly improved their predictive power. We reviewed the clinical history, electrocardiogram, and echocardiogram of 31 patients (age 1.9 +/- 1. 73 years) who underwent catheterization for isolated VSD. Noninvasive data were compared for patients with mean PAP <20 mmHg (group 1) and those with mean PAP > or =20 (group 2) at catheterization. Fourteen (45%) patients were in group 1 and 17 (55%) in group 2. Doppler estimation of VSD gradient, right ventricular hypertrophy by echocardiogram, interventricular septal orientation, and VSD size had predictive value for elevated mean PAP (p < 0.01). All patients (n = 6) with normal findings in all four variables had normal PAP. All patients (n = 12) with at least three of four abnormal findings had elevated PAP. Six patients in group 1 had at least one variable that incorrectly predicted high PAP, whereas 3 patients with normal findings on three of the four variables nevertheless had elevated PAP. No single noninvasive variable accurately predicted PAP in all cases. However, normal findings for all four significant variables did predict normal PAP and suggest that cardiac catheterization is unnecessary in that setting. However, any other combination of normal and abnormal findings for the four significant variables did not reliably predict PAP and such patients may require catheterization to directly measure PAP.

Blood Pressure↗
PubMed2002

Early prediction of acute pancreatitis: prospective study comparing computed tomography scans, Ranson, Glascow, Acute Physiology and Chronic Health Evaluation II scores, and various serum markers.

The aim of this study was to assess the predictability of the outcome of acute pancreatitis using the Ranson, Glascow, and Acute Physiology and Chronic Health Evaluation (APACHE) II scores, the computed tomography (CT) scan, and several serum markers. Altogether, 137 consecutive patients with acute pancreatitis confirmed by CT scan were prospectively included. Blood samples were obtained daily for 6 days. The predictive value of each parameter was studied by univariate and multivariate analyses comparing mild and severe pancreatitis. A total of 111 attacks were graded as mild (81%) and 26 as severe (19%). Ranson (p = 0.3) and APACHE II (p = 0.049) scores appeared insufficiently predictive in the univariate analysis. Pancreatic imaging by CT scan was insufficiently predictive (p > 0.05), whereas the presence of extrapancreatic fluid collections was more indicative of outcome (p <0.05). With the univariate analysis, the four most reliable serum markers were pancreatic amylase (p <0.001), neutrophil elastase (p <0.05), albumin (p <0.002), and C-reactive protein (p <0.001). Results became homogeneous when the CT results were added; serum albumin plus extrapancreatic fluid collections (negative predictive value 92%-96% and positive predictive value 67%-100%) comprised the best indicator of severity. None of the parameters tested achieved sufficient predictability when used alone. Serum albumin plus extrapancreatic fluid collections comprise the best indicator of severity at the time of admission.

APACHE↗
PubMed2006

Quantitative prediction of contrast enhancement from test bolus data in cardiac MSCT.

The purpose of this study was to evaluate a new algorithm for the prediction of contrast enhancement from test bolus data in cardiac multislice spiral computed tomography (MSCT). An algorithm for the prediction of contrast enhancement using test bolus data was developed. A total of 30 consecutive patients (15 male, 69.5 +/- 9.6 years) underwent cardiac MSCT (12 x 0.75 mm, 120 kV, 500 mAs(eff.)) with a biphasic contrast material injection protocol. Contrast timing was derived from a standard 20 ml test bolus injection. Based on the test bolus time attenuation curves, expected enhancement values were computed for the ascending and descending aorta and the pulmonary trunk and compared with measured data from the cardiac CT scan. At the level of the test bolus measurement in the ascending aorta, the corresponding attenuation values were 309.4 +/- 49.6 Hounsfield Units (HU) for the predicted and 285.6 +/- 42.6 HU for the measured attenuation, respectively. The mean deviation between predicted and measured CT values was 32.8 +/- 48.2 HU (upper and lower limits of agreement 101.4/-53.8 HU), indicating a slight systematic tendency for overestimation. For 80% of the patients the prediction error was less than 50 HU. Prediction of contrast enhancement in cardiac MSCT from test bolus data is feasible with a relatively small mean deviation; 80% of the predictions were within a range that might be acceptable for routine clinical application.

Aged↗