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PubMed2004

A validated clinical model to predict the need for admission and length of stay in children with acute bronchiolitis.

OBJECTIVE: To develop and validate a logistic regression model to predict need for admission and length of hospital stay in children presenting to the Emergency Department with bronchiolitis. SETTING: Two children's hospitals in Dublin, Ireland. METHODS: We reviewed 118 episodes of bronchiolitis in 99 children admitted from the Emergency Department. Those discharged within 24 h by a consultant/attending paediatrician were retrospectively categorized as suitable for discharge. We then validated the model using a cohort of 182 affected infants from another paediatric Emergency Department in a bronchiolitis season 2 years later. In the validation phase actual admission, failed discharge, and age less than 2 months defined the need for admission. RESULTS: The model predicted admission with 91% sensitivity and 83% specificity in the validation cohort. Age [odds ratio (OR) 0.86, 95% confidence interval (CI) 0.76-0.97], dehydration (OR 2.54, 95% CI 1.34-4.82), increased work of breathing (OR 3.39, 95% CI 1.29-8.92) and initial heart rate above the 97th centile (OR 3.78, 95% CI 1.05-13.57) predicted the need for admission and a longer hospital stay. CONCLUSION: We derived and validated a severity of illness model for bronchiolitis. This can be used for outcome prediction in decision support tools or severity of illness stratification in research/audit.

Acute Disease↗

PubMed2004

A computer model to predict the outcome and duration of ureteral or renal calculous passage.

PURPOSE: We developed a computer model to predict the outcome and the duration until passage of ureteral/renal calculi. MATERIALS AND METHODS: A retrospective, randomized study was performed of the outcome in 301 patients presenting to the emergency room for renal colic. Presenting characteristics of those diagnosed with a single calculus by computerized tomography were recorded for analysis. Predictors of stone passage and passage duration were identified and then used to create a logistic regression model. The algorithm was trained on 141 randomly selected patients and then tested on a separate 160 patients. Model accuracy was compared to predictions from 10 experienced urologists and 9 urology residents in 77 randomly selected patients. The model was tested further in 30 randomly selected patients at a private hospital to assess its general applicability. RESULTS: The model prediction accuracy in 160 patients was 86.3% for passage and 87.3% for duration (less or greater than 2 weeks). In the comparison group the model, the 10 experienced urologists and the 9 urology residents had an overall prediction accuracy of 88.3%, 70.5% (p = 0.006) and 72% (p = 0.007) for passage, and 87.1%, 71.6% (p = 0.007) and 81% (p = 0.075) for duration, respectively. Prediction accuracy was 93.3% for passage and 90.3% for duration when tested at a private hospital. CONCLUSIONS: Our model provides outcome and duration of passage predictions for patients presenting acutely in the emergency room with a single ureteral/renal calculus. It performs better than experienced urologists and urology residents. It can be applied to a private practice setting with equal accuracy.

Adolescent↗

PubMed1998

Levofloxacin population pharmacokinetics and creation of a demographic model for prediction of individual drug clearance in patients with serious community-acquired infection.

Population pharmacokinetic modeling is a useful approach to obtaining estimates of both population and individual pharmacokinetic parameter values. The potential for relating pharmacokinetic parameters to pharmacodynamic outcome variables, such as efficacy and toxicity, exists. A logistic regression relationship between the probability of a successful clinical and microbiological outcome and the peak concentration-to-MIC ratio (and also the area under the plasma concentration-time curve [AUC]/MIC ratio) has previously been developed for levofloxacin; however, levofloxacin assays for determination of the concentration in plasma are not readily available. We attempted to derive and validate demographic variable models to allow prediction of the peak concentration in plasma and clearance (CL) from plasma for levofloxacin. Two hundred seventy-two patients received levofloxacin intravenously for the treatment of community-acquired infection of the respiratory tract, skin or soft tissue, or urinary tract, and concentrations in plasma, guided by optimal sampling theory, were obtained. Patient data were analyzed by the Non-Parametric Expectation Maximization approach. Maximum a posteriori probability Bayesian estimation was used to generate individual parameter values, including CL. Peak concentrations were simulated from these estimates. The first 172 patients were used to produce demographic models for the prediction of CL and the peak concentration. The remaining 100 patients served as the validation group for the model. A median bias and median precision were calculated. A two-compartment model was used for the population pharmacokinetic analysis. The mean CL and the mean volume of distribution of the central compartment (V1) were 9.27 liters/h and 0.836 liter/kg, respectively. The mean values for the intercompartmental rate constants, the rate constant from the central compartment to the peripheral compartment (Kcp) and the rate constant from the peripheral compartment to the central compartment (Kpc), were 0.487 and 0.647 h(-1), respectively. The mean peak concentration and the mean AUC values normalized to a dosage of 500 mg every 24 h were 8.67 microg/ml and 72.53 microg x h/ml, respectively. The variables included in the final model for the prediction of CL were creatinine clearance (CLCR), race, and age. The median bias and median precision were 0.5 and 18.3%, respectively. Peak concentrations were predicted by using the demographic model-predicted parameters of CL, V1, Kcp and Kpc, in the simulation. The median bias and the median precision were 3.3 and 21.8%, respectively. A population model of the disposition of levofloxacin has been developed. Population demographic models for the prediction of peak concentration and CL from plasma have also been successfully developed. However, the performance of the model for the prediction of peak concentration was likely insufficient to be of adequate clinical utility. The model for the prediction of CL was relatively robust, with acceptable bias and precision, and explained a reasonable amount of the variance in the CL of levofloxacin from plasma in the population (r2 = 0.396). Estimated CLCR, age, and race were the final model covariates, with CLCR explaining most of the population variance in the CL of levofloxacin from plasma. This model can potentially optimize the benefit derived from the pharmacodynamic relationships previously developed for levofloxacin.

Adult↗

PubMed2003

Clinical application of artificial neural network (ANN) modeling to predict pharmacokinetic parameters of severely ill patients.

Artificial neural network (ANN) modeling was used to evaluate the pharmacokinetics of aminoglycosides (arbekacin sulfate and amikacin sulfate) in severely ill patients. The plasma level was predicted by ANN modeling using parameters related to the severity of the patient's condition and the predictive performance was shown to be better than could be achieved using multiple regression analysis. These results indicate that there is a non-linear relationship between the pharmacokinetics of aminoglycosides and the severity of the patient's condition, and this should be taken into account when determining the dose for severely ill patients. Patients whose plasma levels are likely to fall below the effective level can be identified by ANN modeling with a predictive sensitivity and specificity superior to multivariate logistic regression analysis. The predictable range should be inferred from the data structure before the modeling in order to improve the predictive performance. The volume of distribution (Vd) in the normal range was weakly predicted by ANN modeling from the patients' data. Prediction of clearance by ANN modeling was poorer than that obtained from serum creatinine concentration by linear regression analysis. These results suggest that the input-output relationship (linear or non-linear) should be taken into account in selecting the modeling method. Linear modeling can give better predictive performance for linear systems and non-linear modeling can give better predictive performance for non-linear systems. In general, the performance of ANN modeling was superior to linear modeling for PK/PD prediction. For accurate modeling, a predictable range should be inferred from the data structure before the analysis. Restriction of the predictable region, as determined from the data structure, produced an increase in prediction performance. When applying ANN modeling in clinical settings, the predictive performance and predictable region should be investigated in detail to avoid the risk of harm to severely ill patients.

Critical Illness↗

PubMed2000

External validation of the templeton model for predicting success after IVF.

This study aimed to externally validate the prognostic model presented by Templeton in 1996 for live births resulting from IVF treatment. Data were used from the University Hospital, Nijmegen, The Netherlands, from March 1991 to January 1999. The predictive capacity of the model in our population discriminated between those women with a low probability of success and those with a relatively high probability. Despite these encouraging findings, our data show that implementation of the model in clinical decision-making remains difficult. The Templeton model is not applicable or usable in daily clinical practice, because the model did not give more information about the prognosis for the vast majority of the patients. Therefore, the search for better prognostic factors resulting in better predictive models should continue.

Adult↗

PubMed2005

Comparison of 3 clinical models for predicting the probability of pulmonary embolism.

Two clinical models have been described to predict the probability of pulmonary embolism: the Canadian (or Wells) model, and the Geneva model. A third model has been developed recently at our institution (the Pisa model). We compared the performance of the 3 models in 215 consecutive patients with suspected pulmonary embolism. The clinical probability predicted by the models was categorized as low, intermediate, or high. In all patients, pulmonary angiography was used as the reference diagnostic standard. In patients with pulmonary embolism, the extent of pulmonary embolization was assessed on the lung scan as an index of disease severity. The prevalence of pulmonary embolism was 43.3%, and the median extent of pulmonary embolization at diagnosis was 39.8% (range, 4.5%-75.3%). The proportions of patients categorized as having low, intermediate, or high probability were, respectively: 12%, 60%, and 28%, for the Geneva model; 30%, 55%, and 15%, for the Wells model; 37%, 37%, and 26% for the Pisa model. The frequencies of pulmonary embolism in the low, intermediate, and high probability categories were, respectively: 50%, 39%, and 49% for the Geneva model; 12%, 54%, and 64% for the Wells model; 5%, 42%, and 98% for the Pisa model. Among patients with pulmonary embolism, there was a strong, positive relation between clinical probability predicted by the Pisa model and the extent of pulmonary embolization. The Pisa model proved more accurate than the 2 other models. It may be useful to physicians in defining precisely the pretest probability of pulmonary embolism.

Adolescent↗

PubMed2005

Can biological tests assist prediction of suicide in mood disorders?

Predicting suicide is difficult due to its low base-rate and the limited specificity of clinical predictors. Prospective biological studies suggest that dysfunctions in the serotonergic system and hypothalamic-pituitary-adrenal axis have some predictive power for completed suicide in mood disorders. A prediction model that incorporates biological testing to increase specificity and sensitivity of prediction of suicide is of potential clinical value. Meta-analyses of prospective biological studies of suicide and cerebrospinal fluid 5-hydroxyindoleacetic acid (CSF 5-HIAA) and suicide and the dexamethasone suppression test (DST) in mood disorders using the penalized quasi-likelihood (PQL) and bootstrap method yield odds ratios for prediction of suicide of 4.48 and 4.65 respectively. Two combinatory prediction models, the first requiring positive results on more than one test, and the second requiring a positive result on either one of two tests, were tested to assess their sensitivity, specificity, and predictive power using biological data from published and unpublished studies. The prediction model that requires both DST and CSF 5-HIAA tests to be positive results in 37.5% sensitivity, 88% specificity, and has a positive predictive value of 23%. The prediction model that requires either DST or CSF 5-HIAA tests to be positive results in 87.5% sensitivity, 28% specificity, and has a positive predictive value of 10%. Thus, models attempting to predict a lethal outcome that is uncommon perform very differently making model choice of major importance. Further work on refining biological predictors and integration with clinical predictors is needed to optimize a model to predict suicide in the clinic.

Biomarkers↗

PubMed2001

Birth weight prediction by three-dimensional ultrasonography: fractional limb volume.

OBJECTIVE: To introduce fractional limb volume as a new ultrasonographic parameter, validate reliability of fractional limb volume measurements, develop new birth weight prediction models, and examine their practical utility for estimating fetal weight during late pregnancy. METHODS: Healthy late-third-trimester fetuses were prospectively scanned by two- and three-dimensional ultrasonography within 4 days of delivery. Volume data sets were subsequently used to extract several standard ultrasonographic measurements. Fractional limb volumes of the upper arm and thigh were based on 50% of diaphyseal bone length. Intraclass correlation was used to analyze interobserver and intraobserver reliability of fractional limb volume measurements. Several weight prediction models were developed by linear regression analysis. New prediction models were prospectively compared with the Hadlock formula in 30 healthy late-third-trimester fetuses. RESULTS: One hundred fetuses were scanned at a mean +/- SD menstrual age of 39.2 +/- 1.2 weeks. Intraclass correlation indicated a significant degree of interobserver and intraobserver reliability for fractional thigh volume. Fractional thigh volume (r = 0.86), fractional upper arm volume (r = 0.83), abdominal circumference (r = 0.83), and midthigh circumference (r = 0.82) were most highly correlated with birth weight. The best prediction model (abdominal circumference and fractional thigh volume) gave weight estimates that deviated from actual birth weight by -0.025% +/- 7.8%. For late-third-trimester fetuses, the Hadlock model yielded errors of 9.0% +/- 9.0%. Prospective testing confirmed superior performance of the new prediction model, which gave accuracy of 2.3% +/- 6.6% (Hadlock method, 8.4% +/- 8.7%). It correctly predicted 20 of 30 birth weights to within 5% of actual weight. By comparison, the Hadlock model predicted only 6 of 30 birth weights to within 5% of actual weight. CONCLUSIONS: A new birth weight prediction model, based on fractional thigh volume and abdominal circumference, is reliable during the late third trimester. It provides a means for including soft tissue evaluation for birth weight prediction. This rapid technique avoids technical limitations that currently hinder the practical implementation of three-dimensional ultrasonography for estimating birth weight.

Birth Weight↗

PubMed1995

A model for predicting central nervous system oxygen toxicity from hyperbaric oxygen exposures in humans.

Under certain circumstances, Navy divers breathe 100% O2 when working underwater. Serious symptoms of central nervous system (CNS) O2 toxicity can develop from hyperbaric O2 exposure; immersion and exercise are also known to exacerbate toxicity. We developed risk models for quantitative prediction of the probability of developing symptoms using a large set of human data in which occupational exposure conditions were simulated. Exposures were 5 to 265 min at PO2 levels from 20 to 50 feet of sea water (fsw) (1 fsw = 3.06 kPa). Approximately half of the exposures were to a single PO2, while the remainder were more complicated consisting of exposures to multiple levels of hyperbaric O2. In 688 trials, there were 42 exposure-stopping symptoms. We used maximum likelihood to estimate parameters, likelihood ratios to compare model fits, and chi 2 tests to judge goodness-of-fit of model predictions to observations. The modeling shows that risk has a steep PO2 dependence. A model with autocatalytic features fits the data as well as a simpler model: when PO2 is elevated beyond 34 fsw, risk accumulates rapidly without bound while accumulating toward an asymptote at lower PO2 levels. This autocatalytic feature of risk accumulation implies a testable hypothesis that substantial protection from human CNS O2 toxicity can be obtained from intermittent exposure (periodic exposure to lower PO2). The models predict that the probability of O2 toxicity is less than 7% with current Navy limits while breathing 95% O2. Probability of symptoms is < 1% if FIO2 is maintained at the United States Navy recommended level of 75%.

Central Nervous System↗

PubMed2004

On the use of multivariable piecewise-linear models for predicting human response to anesthesia.

The standard modeling paradigm used to describe the relationship between input anesthetic agents and output patient endpoint variables are single-input single-output pharmacokinetic-pharmacodynamic (PK-PD) compartment models. In this paper, we propose the use of multivariable piecewise-linear models to describe the relations between inputs that include anesthesia, surgical stimuli and disturbances to a variety of patient output variables. Subspace identification methods are applied to clinical data to construct the models. A comparison of predicted and measured responses is completed, which includes predictions from PK-PD models, and piecewise-linear time-invariant models.

Adult↗

PubMed2001

Response to first drug trial predicts outcome in childhood temporal lobe epilepsy.

OBJECTIVE: To construct a clinical prediction model for the early identification of children destined to develop refractory temporal lobe epilepsy (TLE) 2 years after epilepsy onset. METHODS: Patients with TLE between 1 and 18 years old seen in the Division of Neurology at Children's Hospital of Philadelphia during 1999 were identified through billing records and chart review. Data were abstracted independently on 5 candidate predictor variables for refractory TLE and on seizure frequency outcome at 2 years after epilepsy onset. RESULTS: One hundred twenty patients met inclusion criteria and had at least 2 years of follow-up. Forty-five of 120 patients (37.5%) had refractory TLE at 2 years after onset, and 75 of 120 (62.5%) were seizure free. Three significant predictors of refractory TLE were found on bivariate analysis: an early risk factor for epilepsy (risk ratio = 3.5 [95% CI 2.2, 5.6]), temporal lobe abnormality on MRI scan (2.9 [95% CI 1.9, 4.6]), and failure of the first antiepileptic drug (AED) trial (16.5 [95% CI 6.3, 43.9]). Logistic regression indicated that the best model to predict refractory TLE contained only the variable "failure of first AED trial," with a positive predictive value of 0.89 (95% CI 0.76, 0.96) and negative predictive value of 0.95 (95% CI 0.87, 0.99) to predict "refractory TLE" at 2 years. CONCLUSIONS: Failure of first AED trial accurately predicts refractory TLE at 2 years after onset, based on retrospective cohort data in children. If verified prospectively and with longer follow-up, this finding should support earlier consideration of surgical options.

Anticonvulsants↗

PubMed2003

Predictive thermal inactivation model for effects of temperature, sodium lactate, NaCl, and sodium pyrophosphate on Salmonella serotypes in ground beef.

Analyses of survival data of a mixture of Salmonella spp. at fixed temperatures between 55 degrees C (131 degrees F) and 71.1 degrees C (160 degrees F) in ground beef matrices containing concentrations of salt between 0 and 4.5%, concentrations of sodium pyrophosphate (SPP) between 0 and 0.5%, and concentrations of sodium lactate (NaL) between 0 and 4.5% indicated that heat resistance of Salmonella increases with increasing levels of SPP and salt, except that, for salt, for larger lethalities close to 6.5, the effect of salt was evident only at low temperatures (<64 degrees C). NaL did not seem to affect the heat resistance of Salmonella as much as the effects induced by the other variables studied. An omnibus model for predicting the lethality for given times and temperatures for ground beef matrices within the range studied was developed that reflects the convex survival curves that were observed. However, the standard errors of the predicted lethalities from this models are large, so consequently, a model, specific for predicting the times needed to obtained a lethality of 6.5 log(10), was developed, using estimated results of times derived from the individual survival curves. For the latter model, the coefficient of variation (CV) of predicted times range from about 6 to 25%. For example, at 60 degrees C, when increasing the concentration of salt from 0 to 4.5%, and assuming that the concentration of SPP is 0%, the time to reach a 6.5-log(10) relative reduction is predicted to increase from 20 min (CV = 11%) to 48 min (CV = 15%), a 2.4 factor (CV = 19%). At 71.1 degrees C (160 degrees F) the model predicts that more than 0.5 min is needed to achieve a 6.5-log(10) relative reduction.

Animals↗

PubMed1995

A comparison of four severity-adjusted models to predict mortality after coronary artery bypass graft surgery.

OBJECTIVE: To assess the validity of four severity-adjusted models to predict mortality following coronary artery bypass graft surgery by using an independent surgical database. DESIGN: A prospective observational study wherein predicted mortality for each patient was obtained by using four different published severity-adjusted models. SETTING: A university-affiliated teaching community hospital. PATIENTS: Eight hundred sixty-eight consecutive patients who underwent coronary artery bypass graft surgery without accompanying valve or aneurysm repair during the period from 1991 to 1993. INTERVENTIONS: None. MAIN OUTCOME MEASURES: Predicted mortality rates for each model were obtained by averaging individual patient predictions and were compared with actual morality rates. We assessed the accuracy of overall prediction for the total series, as well as compared individual patient predictions created by each model. The discrimination of models was assessed with receiver operating characteristic curves and the Hosmer-Lemeshow goodness-of-fit statistic. RESULTS: The observed crude mortality rate was 3.7%. The predicted mortality rate ranged from 2.8% to 9.2%, despite relatively good discrimination by the models (area under the receiver operating characteristic curve, 0.70 to 0.74). The individual patient mortality predicted by different models varied by as much as a ninefold difference. CONCLUSIONS: The currently used coronary artery bypass graft predictive models, although generally accurate, have significant shortcomings and should be used with caution. The predicted mortality rate following coronary artery bypass graft surgery varied by a factor of 3.3 from lowest to highest, making the choice of model a critical factor when assessing outcome. The use of these models for individual patient risk estimations is risky because of the marked discrepancies in individual predictions created by each model.

Aged↗

PubMed1993

A model to predict survival at time of postcardiotomy intraaortic balloon pump insertion.

To facilitate timely application of new forms of cardiac support to patients at highest risk after cardiotomy despite conventional support with the intraaortic balloon pump, an accurate prediction of survival must be available at the time of weaning from cardiopulmonary bypass. We, therefore, acquired 240 demographic, disease, and perioperative characteristics of 322 patients (mortality rate, 48.4%) who required IABP support to separate from bypass. Four variables available before or within 10 minutes of the first attempt at weaning from bypass significantly predicted mortality by stepwise logistic regression: complete heart block as demonstrated by need for temporary pacing at weaning (p < 0.001), advanced age (p < 0.002), preoperative blood urea nitrogen concentration (p = 0.036), and female sex (p = 0.048). An equation generated by the logistic model predicted a 72.2% survival rate in the 25% of patients at least risk (actual survival rate, 71.6%); in the 25% at greatest risk, death was predicted in 73.0%, and the actual mortality rate was 74.1%. The equation was then prospectively applied to 330 intraaortic balloon pump-supported patients managed at another institution. The overall mortality rate there was 41.2%; in the 25% at least risk, predicted survival rate was 70.5% (actual survival rate, 77.1%), and in the 25% at greatest risk, predicted mortality rate was 75.7% (actual mortality rate, 62.7%). Thus, retrospectively at one institution and prospectively at another, the equation generated by this model based only on data available at the time of weaning from bypass was able to define one subgroup of patients 2.6 to 2.7 times as likely to die as another subgroup from within similar cohorts.(ABSTRACT TRUNCATED AT 250 WORDS)

Aged↗

PubMed2006

Predicting the duration of sickness absence for patients with common mental disorders in occupational health care.

OBJECTIVES: This study attempted to determine the factors that best predict the duration of absence from work among employees with common mental disorders. METHODS: A cohort of 188 employees, of whom 102 were teachers, on sick leave with common mental disorders was followed for 1 year. Only information potentially available to the occupational physician during a first consultation was included in the predictive model. The predictive power of the variables was tested using Cox's regression analysis with a stepwise backward selection procedure. The hazard ratios (HR) from the final model were used to deduce a simple prediction rule. The resulting prognostic scores were then used to predict the probability of not returning to work after 3, 6, and 12 months. Calculating the area under the curve from the ROC (receiver operating characteristic) curve tested the discriminative ability of the prediction rule. RESULTS: The final Cox's regression model produced the following four predictors of a longer time until return to work: age older than 50 years [HR 0.5, 95% confidence interval (95% CI) 0.3-0.8], expectation of duration absence longer than 3 months (HR 0.5, 95% CI 0.3-0.8), higher educational level (HR 0.5, 95% CI 0.3-0.8), and diagnosis depression or anxiety disorder (HR 0.7, 95% CI 0.4-0.9). The resulting prognostic score yielded areas under the curves ranging from 0.68 to 0.73, which represent acceptable discrimination of the rule. CONCLUSIONS: A prediction rule based on four simple variables can be used by occupational physicians to identify unfavorable cases and to predict the duration of sickness absence.

Adjustment Disorders↗

PubMed2002

External validation of a prognostic model for predicting survival of cirrhotic patients with refractory ascites.

OBJECTIVE: Cirrhotic patients with refractory ascites (RA) have a poor prognosis, although individual survival varies greatly. A model that could predict survival for patients with RA would be helpful in planning treatment. Moreover, in cases of potential liver transplantation, a model of these characteristics would provide the bases for establishing priorities of organ allocation and the selection of patients for a living donor graft. Recently, we developed a model to predict survival of patients with RA. The aim of this study was to establish its generalizability for predicting the survival of patients with RA. METHODS: The model was validated by assessing its performance in an external cohort of patients with RA included in a multicenter, randomized, controlled trial that compared large-volume paracentesis and peritoneovenous shunt. The values for actual and model-predicted survival of three risk groups of patients, established according to the model, were compared graphically and by means of the one-sample log-rank test. RESULTS: The model provided a very good fit to the survival data of the three risk groups in the validation cohort. We also found good agreement between the survival predicted from the model and the observed survival when patients treated with peritoneovenous shunt and with paracentesis were considered separately. CONCLUSION: Our survival model can be used to predict the survival of patients with RA and may be a useful tool in clinical decision making, especially in deciding priority for liver transplantation.

Actuarial Analysis↗

PubMed2003

A model to predict survival at one month, one year, and five years after liver transplantation based on pretransplant clinical characteristics.

Reliable models that could predict outcome of liver transplantation (LT) may guide physicians to advise their patients of immediate and late survival chances and may help them to optimize organ use. The objective of this study was to develop user-friendly models to predict short and long-term mortality after LT in adults based on pre-LT recipient characteristics. The United Network for Organ Sharing (UNOS) transplant registry (n = 38,876) from 1987 to 2001 was used to develop and validate the model. Two thirds of patients were randomized to develop the model (the modeling group), and the remaining third was randomized to cross-validate (the cross-validation group) it. Three separate models, using multivariate logistic regression analysis, were created and validated to predict survival at 1 month, 1 year, and 5 years. Using the total severity scores of patients in the modeling group, a predictive model then was created, and the predicted probability of death as a function of total score then was compared in the cross-validation group. The independent variables that were found to be very significant for 1 month and 1 year survival were age, body mass index (BMI), UNOS status 1, etiology, serum bilirubin (for 1 month and 1 year only), creatinine, and race (only for 5 years). The actual deaths in the cross-validation group followed very closely the predicted survival graph. The chi-squared goodness-of-fit test confirmed that the model could predict mortality reliably at 1 month, 1 year, and 5 years. We have developed and validated user-friendly models that could reliably predict short-term and long-term survival after LT.

Adult↗

PubMed1983

A predictive value model for quality control: effects of the prevalence of errors on the performance of control procedures.

A predictive value model has been developed to describe the usefulness of results from quality control tests or procedures. The model shows that the critical parameters are the probability for false rejection, probability for error detection, and prevalence or frequency of occurrence of analytical errors. When prevalence is low, control procedures should have a low probability for false rejection. When prevalence is high, control procedures should have a high probability for error detection. The predictive value model for a quality control (QC) test is analogous to the predictive value model for a diagnostic test, thus suggesting new strategies for optimizing the performance of QC tests.

Diagnostic Errors↗