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Incorporating predictions of individual patient risk in clinical trials.

A risk prediction model is a statistical technique that gives a predicted probability of a certain event for an individual patient. Prediction models outperform the traditional risk classification systems that work by assigning patients into risk groups based on the presence or absence of particular risk factors, such as stage of disease. As such, risk prediction models have a number of important possible uses in clinical trials. For Phase II studies, prediction models can help adjust comparisons with historical control groups for differences in case mix. For Phase III studies, prediction models can ensure that accrued patients are at sufficiently high risk. This improves statistical power and avoids unethical inclusion of low-risk patients. We also propose that prediction models could potentially be used for applying the results of Phase III trials to individual patients. Clinical decisions could be informed by individualized estimates of treatment benefit, rather than by average treatment effects.

Clinical Trials, Phase III as Topic↗
PubMed1996

Predicted and measured resting metabolic rate of male and female endurance athletes.

OBJECTIVE: To measure the resting metabolic rate (RMR) of a group of endurance-trained male and female athletes and to compare it with values predicted using published equations. DESIGN: RMR was measured twice: 1 week apart for the men and approximately 1 month apart for the women. RMR was predicted using equations of Harris and Benedict, Owen et al, Mifflin et al, and Cunningham. SUBJECTS/SETTING: Subjects were 37 trained endurance athletes (24 men, 13 women) who had participated in studies previously completed in our laboratory. MAIN OUTCOME MEASURES: The primary outcome measure was the comparison of predicted RMR with measured RMR. An exploratory procedure for the determination of predictive variables in these athletes was also performed. STATISTICAL ANALYSES PERFORMED: The Root Mean Squared Prediction Error method was used to compare predicted RMR with measured RMR. The maximum R2 procedure method was used to determine the best possible combination of four variables that explained the largest amount of variance in RMR. RESULTS: The Cunningham equation was found to predict measured RMR most accurately (within 158 kcal/d for men and 103 kcal/d for women). Fat-free mass was the best predictor of RMR in men, whereas energy intake was the best predictor in women. APPLICATIONS/CONCLUSIONS: The Cunningham equation provides an accurate estimate of RMR when determining energy needs of highly active people. Equations specific to athletes need to be developed. Factors in addition to body weight, height, and age should be investigated as possible predictor variables in athletes.

Adult↗
PubMed1997

Cross-validation of prediction equations for resting energy expenditure in young, healthy children.

OBJECTIVE: To examine the accuracy of several prediction equations for resting energy expenditure (REE) in children. DESIGN: REE was measured in 113 prepubertal children (60 girls and 53 boys aged 3.9 to 7.8 years old, weighing 14.7 to 30.0 kg) using indirect calorimetry and compared with values estimated from the prediction equations of Altman and Dittmer, The Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU), Maffeis et al, and Harris and Benedict. STATISTICAL ANALYSIS: Measured REE (MREE) was compared with predicted REE (PREE) by means of regression analysis. Prediction equations were considered accurate if the regression of MREE vs PREE was not significantly different from the line of identity (slope=1.0; intercept=0). Precision was assessed by the multiple correlation coefficient of the regression of MREE vs PREE. RESULTS: MREE was 938+/-119 kcal/day, and PREE was 1,057+/-224 kcal/day for the Altman and Dittmer equations, 956+/-84 kcal/day for the FAO/WHO/UNU equations, 948+/-64 kcal/day for the equations of Maffeis et al, and 954+/-102 kcal/day for the Harris-Benedict equations. The regression of MREE vs PREE was significantly different from the line of identity for all prediction equations except the FAO/WHO/UNU equations (slope=0.96, P=.735; intercept=-15 kcal/day, P=.885 for girls and slope=1.08, P=.635; intercept=-62 kcal/day, P=.635 for boys). None of the equations was precise for MREE vs PREE (for all, R2<.6). For the FAO/WHO/UNU equations, less than half of the predictions were within +/-50 kcal/day but 99% were within 200 kcal/day. CONCLUSION: Most prediction equations for REE in children do not accurately or precisely estimate REEs. The exception is the FAO/WHO/UNU equations, which are reasonably accurate and precise for practical purposes.

Calorimetry, Indirect↗
PubMed1987

A model for predicting crocidolite fiber size distributions.

To minimize the problems of sample mass inconsistency associated with limited fiber counts, a model has been developed to predict the particle size distribution for crocidolite asbestos fibers with lengths exceeding the median log length value. With this model, it is possible to predict the dimensions of many more fibers than it is practical to measure by typical electron microscopic techniques. Thus, sample mass extrapolations, required for animal implantation studies, can be based on a much larger number of particles, reducing error that could occur, for example, from the presence of one or two very large particles. The model assumes a linear relationship between length and width. The number of fibers predicted in each length category is determined from the measured data. Particle lengths were randomly chosen within size classes, and the corresponding widths were predicted using the model. The similarity of the predicted population to the actual population was tested by comparing the width distributions using the chi-square test at the 90% confidence level. Model populations ranging from 250 to 10,000 particles were predicted. Those populations with 2500 or more fibers were similar to the actual sample in their size characteristics. Predicted populations in excess of 7500 fibers were required to produce consistent sample masses.

Asbestos↗
PubMed2003

Experimental evaluation of an elastic foundation model to predict contact pressures in knee replacements.

Computational wear prediction is an attractive concept for evaluating new total knee replacement designs prior to physical testing and implementation. An important hurdle to such technology is the lack of in vivo contact pressure predictions. To address this issue, this study evaluates a computationally efficient simulation approach that combines the advantages of rigid and deformable body modeling. The hybrid method uses rigid body dynamics to predict body positions and orientations and elastic foundation theory to predict contact pressures between general three-dimensional surfaces. To evaluate the method, we performed static pressure experiments with a commercial knee implant in neutral alignment using flexion angles of 0, 30, 60, and 90 degrees and loads of 750, 1500, 2250, and 3000N. Using manufacturer CAD geometry for the same implant, an elastic foundation model with linear or nonlinear polyethylene material properties was implemented within a commercial multibody dynamics software program. The model's ability to predict experimental peak and average contact pressures simultaneously was evaluated by performing dynamic simulations to find the static configuration. Both the linear and nonlinear material models predicted the average contact pressure data well, while only the linear material model could simultaneously predict the trends in the peak contact pressure data. This novel modeling approach is sufficiently fast and accurate to be used in design sensitivity and optimization studies of knee implant mechanics and ultimately wear.

Compressive Strength↗
PubMed1998

Dynamic knee loads during gait predict proximal tibial bone distribution.

This study tested the validity of the prediction of dynamic knee loads based on gait measurements. The relationship between the predicted loads at the knee and the distribution of bone between the medial and lateral sides of the tibia was examined. The motion and external forces and moments at the knee were measured during gait and a statically determinate muscle model was used to predict the corresponding forces on the medial and lateral tibial plateaus. In particular, the relationship between the knee adduction moment during gait and the ratio or distribution of medial to lateral tibial bone mineral content was studied. Bone mineral content was measured with dual energy X-ray absorptiometry in four regions, two proximal regions 20 mm in height, one medial and one lateral and two distal regions 20 mm in height, one medial and one lateral. The best single predictor of the medial lateral ratio of proximal bone mineral content (bone distribution) was the adduction moment (R2=0.31, p=0.003). Adding weight (negative coefficient. p=0.0004) and the ratio of the average predicted peak force on the medial plateau to the predicted peak force on the lateral plateau (positive coefficient, p=0.0033) to the regression model significantly increased the ability to predict the proximal medial lateral bone distribution (R2=0.72, p=0.0001). Distally neither the subject characteristics nor the gait moments and predicted forces were significant predictors of the bone distribution. The lack of a correlation distally may be reflective of the forces being more evenly distributed further from the tibial plateau. While it has long been suggested that the adduction moment is the primary determinate of the distribution of load between the medial and lateral plateaus, this is the first evidence of its relationship to the underlying bone distribution.

Absorptiometry, Photon↗
PubMed1999

Femoral strength is better predicted by finite element models than QCT and DXA.

Clinicians and patients would benefit if accurate methods of predicting and monitoring bone strength in-vivo were available. A group of 51 human femurs (age range 21-93; 23 females, 28 males) were evaluated for bone density and geometry using quantitative computed tomography (QCT) and dual energy X-ray absorptiometry (DXA). Regional bone density and dimensions obtained from QCT and DXA were used to develop statistical models to predict femoral strength ex vivo. The QCT data also formed the basis of a three-dimensional finite element (FE) models to predict structural stiffness. The femurs were separated into two groups; a model training set (n = 25) was used to develop statistical models to predict ultimate load, and a test set (n = 26) was used to validate these models. The main goal of this study was to test the ability of DXA, QCT and FE techniques to predict fracture load non-invasively, in a simple load configuration which produces predominantly femoral neck fractures. The load configuration simulated the single stance phase portion of normal gait; in 87% of the specimens, clinical appearing sub-capital fractures were produced. The training/test study design provided a tool to validate that the predictive models were reliable when used on specimens with "unknown" strength characteristics. The FE method explained at least 20% more of the variance in strength than the DXA models. Planned refinements of the FE technique are expected to further improve these results. Three-dimensional FE models are a promising method for predicting fracture load, and may be useful in monitoring strength changes in vivo.

Absorptiometry, Photon↗
PubMed2002

Evaluation of structural and evolutionary contributions to deleterious mutation prediction.

Methods for automated prediction of deleterious protein mutations have utilized both structural and evolutionary information but the relative contribution of these two factors remains unclear. To address this, we have used a variety of structural and evolutionary features to create simple deleterious mutation models that have been tested on both experimental mutagenesis and human allele data. We find that the most accurate predictions are obtained using a solvent-accessibility term, the C(beta) density, and a score derived from homologous sequences, SIFT. A classification tree using these two features has a cross-validated prediction error of 20.5% on an experimental mutagenesis test set when the prior probability for deleterious and neutral cases is equal, whereas this prediction error is 28.8% and 22.2% using either the C(beta) density or SIFT alone. The improvement imparted by structure increases when fewer homologs are available: when restricted to three homologs the prediction error improves from 26.9% using SIFT alone to 22.4% using SIFT and the C(beta) density, or 24.8% using SIFT and a noisy C(beta) density term approximating the inaccuracy of ab initio structures modeled by the Rosetta method. We conclude that methods for deleterious mutation prediction should include structural information when fewer than five to ten homologs are available, and that ab initio predicted structures may soon be useful in such cases when high-resolution structures are unavailable.

Bacterial Proteins↗
PubMed1995

Somatosensory evoked potentials for prediction of outcome in acute severe brain injury.

The purpose of this study was to evaluate prospectively short-latency somatosensory evoked potentials (SEPs) as a predictor of outcome in acute, severe brain injury, and to compare this with the predictive power of the motor component of the Glasgow Coma Scale score and computed tomographic scan. Outcome was measured with the Glasgow Outcome Scale at a minimum of 6 months after injury. We studied 109 patients (aged 0.1 to 16.8 years) with SEPs within 4 days of the onset of coma. Four patients had absent SEPs and a favorable outcome by the Glasgow Outcome Scale (full recovery or moderate disability); two of these patients had meningitis with bilateral subdural effusions, one had a midbrain hemorrhage, and one had a decompressive craniectomy for uncontrolled intracranial hypertension. Normal SEPs had a positive predictive value for favorable outcome of 93% (95% confidence interval (CI), 77% to 99%), and absent SEPs had a positive predictive value for unfavorable outcome by the Glasgow Outcome Scale (severe disability, survival in a persistent vegetative state, or death) of 92% (95% CI, 80% to 98%). If the above identifiable clinical situations in which a physical barrier existed to impede cutaneous reception of the electrical impulse were excluded, the positive predictive value of absent SEPs for poor outcome reached 100% (95% CI, 92% to 100%). An absent motor response to painful stimulus also had 100% positive predictive value (95% CI, 84% to 100%) for unfavorable outcome; however, 23% of patients could not be evaluated because of the effects of muscle relaxants or sedatives. In patients with traumatic brain injury, results of computed tomography did not reliably predict outcome. Of the 59 patients with unfavorable outcome, 76% could be identified with SEPs compared with 36% with examination of motor function. We suggest that SEPs be performed in children with acute severe brain injury because they add an important tool to the physician's prognostic armamentarium. We conclude that in the absence of the above mentioned identifiable clinical situations, absent SEPs predict 100% unfavorable outcome, and this finding may warrant consideration of withdrawal of treatment in children with brain injuries.

Acute Disease↗
PubMed1999

Diffusing capacity predicts operative mortality but not long-term survival after resection for lung cancer.

OBJECTIVES: We sought to determine whether diffusing capacity influences operative mortality and long-term survival after resection for lung cancer. METHODS: We retrospectively reviewed the case histories of patients who underwent major resection for lung cancer. The association between operative mortality and predicted postoperative diffusing capacity was examined. Long-term survival among operative survivors was compared between the groups with high and low predicted postoperative diffusing capacity. RESULTS: The group comprised 410 patients with a mean age of 62.3 years. We performed 273 lobectomies, 35 bilobectomies, and 102 pneumonectomies. A total of 32 operative deaths (7.8%) were associated with low predicted postoperative diffusing capacity (P <.001). If we examine only operative survivors, there is no significant difference in survival data between patients with a predicted postoperative diffusing capacity of less than 50 and those with a predicted figure of 50 or more (stage I, 111 vs 90 months; stage II, 26 vs 32 months; stage IIIa 32 vs 26 months; log rank P >.5 for each). On the basis of the Cox proportional hazards model, predicted postoperative diffusing capacity did not have a statistically significant effect on long-term survival (estimated hazard ratio corresponding to a 20-point decrease in predicted postoperative diffusing capacity = 1. 13; 95% confidence interval: 0.92 to 1.37). CONCLUSION: A poor diffusing capacity is associated with high operative mortality but does not adversely affect long-term survival after major lung resection among operative survivors. Improving the perioperative management of patients undergoing major lung resection may enable inclusion of more patients with reduced diffusing capacity in the candidate pool for surgery, thus maximizing survival for early-stage lung cancer.

Adult↗
PubMed2003

Comparison of percentage of total prostate needle biopsy tissue with cancer to percentage of cores with cancer for predicting PSA recurrence after radical prostatectomy: results from the SEARCH database.

OBJECTIVES: Tumor volume in the prostate needle biopsy is an important prognosticator for patients with prostate cancer. However, the best method to measure tumor volume in the prostate needle biopsy is unknown. We compared the total percentage of biopsy tissue with cancer to the percentage of cores positive for their ability to predict adverse pathologic findings and biochemical failure after radical prostatectomy (RP). METHODS: A retrospective survey of 355 patients from the Shared Equal Access Regional Cancer Hospital database treated with RP between 1990 and 2002 was undertaken. Multivariate analysis was used to compare the percentage of cores and percentage of tissue with cancer to the standard clinical variables of age, prostate-specific antigen (PSA) level, biopsy Gleason score, and clinical stage for their ability to predict positive surgical margins, non-organ-confined disease, seminal vesicle invasion, and time to PSA recurrence after RP. RESULTS: On multivariate analysis, the percentage of tissue with cancer significantly predicted non-organ-confined disease and seminal vesicle invasion, but the percentage of cores did not significantly predict any of the pathologic features examined. In separate multivariate analysis, only the percentage of tissue with cancer, but not the percentage of cores with cancer, significantly predicted PSA failure. Moreover, when compared in the same multivariate analysis, only the percentage of tissue with cancer (hazard ratio 8.25, 95% confidence interval 3.06 to 22.22, P <0.001) was a significant predictor. The area under the receiver operating curves for predicting PSA failure was significantly greater for the percentage of tissue with cancer (0.697) than for the percentage of cores (0.644, P = 0.022). Cutpoints for the percentage of tissue with cancer (less than 20%, 20% to 40%, and greater than 40%) and the percentage of cores (less than 34%, 34% to 50%, greater than 50%) both provided significant preoperative risk stratification for biochemical failure, although the percentage of tissue with cancer cutpoints provided better risk stratification (higher hazard ratios and lower P value). Cutpoints for the percentage of tissue with cancer but not the percentage of cores positive further stratified patients who were at low (P = 0.041), intermediate (P = 0.002), and high (P = 0.023) risk on the basis of the PSA level and biopsy Gleason score. CONCLUSIONS: The percentage of tissue with cancer was better than the percentage of cores at predicting advanced pathologic features and PSA recurrence after RP. Unlike the percentage of cores, the percentage of tissue with cancer cutpoints further stratified low, intermediate, and high-risk patients on the basis of PSA level and biopsy Gleason score. Although the percentage of tissue with cancer is a slightly more cumbersome measurement than the percentage of positive cores, it provided statistically and clinically superior preoperative risk stratification for biochemical failure after RP.

Biopsy, Needle↗
PubMed1999

Comparing predictability between eyes after bilateral laser in situ keratomileusis: a theoretical analysis of simultaneous versus sequential procedures.

OBJECTIVE: To compare the predictability of laser in situ keratomileusis (LASIK) between eyes of individuals to determine whether the refractive result of the first eye is useful in improving fellow eye outcomes. DESIGN: Single-center case series. PARTICIPANTS: One surgeon and 196 eyes of 98 patients. INTERVENTION: All patients received sequential bilateral LASIK. The mean time between procedures was 11.6 days. Attempted corrections ranged from 2.30 to 12.00 diopters (D). MAIN OUTCOME MEASURES: Predictability (achieved minus attempted correction), postoperative manifest refraction, and theoretical postoperative manifest refraction, using a proposed attempted correction on the second eye based on first eye results, were analyzed. RESULTS: At 1 week, 1 month, and 3 months, predictability of the first operated eye was correlated with predictability of the fellow eye (1 week: mean 1st = 0.33 D, mean 2nd = 0.33 D, Pearson coefficient = 0.46, P < 0.0005; 1 month: mean 1st = 0.028 D, mean 2nd = -0.020 D, Pearson coefficient = 0.43, P < 0.0005; 3 months: mean 1st = -0.22 D, mean 2nd = -0.12 D, Pearson coefficient = 0.52, P < 0.0005). At the 3-month follow-up of the second eye, comparing the actual distance from emmetropia with that calculated using a theoretical proposed attempted correction based on the first eye refraction, distance from emmetropia was closer in the theoretical correction group. This finding was stronger in patients with preoperative myopia less than 5.5 D (P = 0.03). For this group, 93% of patients in the proposed attempted correction group would fall within 1.0 D of emmetropia compared to 80% found in the actual outcomes. CONCLUSIONS: The refractive predictability between the two eyes of an individual after LASIK is correlated. Theoretically, therefore, one may be able to achieve correction closer to emmetropia in the second eye by applying the refractive predictability results from the first operated eye. In this study, using a theoretical proposed attempted correction in the second eye based on the first eye outcome, we have shown that better outcomes in the second eye are possible, particularly in low myopes. Thus, it may be advantageous to perform bilateral LASIK sequentially rather than simultaneously, using predictability outcomes from the first operated eye in planning fellow eye treatment. Moreover, waiting approximately 1 week was found to be potentially as effective as waiting longer periods of time between treatments. Further studies are necessary to better assess the actual clinical significance of these findings.

Adult↗
PubMed1996

Nonlinear prediction as a tool for tracking the dynamics of single-trial readiness potentials.

The performance of a voluntary act is preceded by an intrinsic process of intention and preparation accompanied by specific patterns of scalp-recorded EEG. The baseline shift to negativity and the decrease of alpha and beta oscillations prior to movement performance are considered to reflect the motor preparation and are observable even in single-trial EEG records during repetitive voluntary movements. The single-trial features of these patterns are of importance since they would reflect the dynamics of EEG activity during movement performance throughout the experimental session. Since this process is very complex and its dynamics is unclear, we applied a more general approach, i.e. the method of nonlinear prediction (NP) recommended for detection of chaos in the behavior of nonlinear dynamical systems. The NP is based on a library of past patterns of the time series used to compute the prediction of future pattern. If the time series is chaotic, the correlation coefficient between predicted and actual values would decrease as a function of the prediction step. The NP was applied to EEG records containing readiness potentials (RPs) at Fz, Cz, Pz, C3 and C4 during repetitive voluntary finger flexion. Because of nonstationarity, the prediction and the library spanned the same time period. As a result (1) chaotic segments in EEG records were detected; and (2) the sharp jumps of the absolute error between predicted and actual values indicated instances of poor prediction, which were interpreted as indicating new stages of EEG activity related to the preparatory process of motor activity.

Adult↗
PubMed2000

Application of polynomial models to predict growth of mixed cultures of Pseudomonas spp. and Listeria in meat.

Three models for one rapid and one slow growing strain of Pseudomonas fragi and one slow growing strain of P. fluorescens were developed in a meat broth; they were designed to take account of variations in growth and to provide a growth response interval. These models, and another for Listeria monocytogenes (Lm14 model), were used to predict the growth of spoilage Pseudomonas spp. and pathogenic Listeria in meat products. The Pseudomonas and Listeria models provided satisfactory predictions concerning inoculated strains grown in decontaminated beef meat. It was also possible to use the Pseudomonas models to predict the growth of the natural flora (mainly Pseudomonas spp.) of refrigerated meat stored under aerobic conditions. In experiments with mixed populations, three situations were observed: (1) in decontaminated meat, L. monocytogenes inoculated alone grew well at 6 degrees C, and this result was correctly predicted by the model; (2) in decontaminated meat inoculated with Listeria and Pseudomonas strains, L. innocua grew well and was not affected by the presence of Pseudomonas, and the growth of both organisms was correctly predicted by the models; (3) in naturally contaminated meat inoculated with Listeria, the strain did not grow until Pseudomonas had reached the stationary phase. The models satisfactorily predicted the growth of Pseudomonas spp. but not that of Listeria. In conclusion, the Lm14 model cannot be used for refrigerated meat stored aerobically as the results suggest a 'fail-safe' level which may be too high: meat had already reached a spoilage state even though no increase in the level of Listeria was observed. The Pseudomonas models accurately predicted the growth of naturally occurring Pseudomonas spp.

Animals↗
PubMed1999

Predicted and observed growth and toxigenesis by Clostridium botulinum type E in vacuum-packaged fishery product challenge tests.

The observed growth and toxigenesis by Clostridium botulinum type E in vacuum-packaged unprocessed, raw pickled and cold-smoked rainbow trout stored at slightly abusive temperatures were compared to predictions generated by two currently available predictive microbiological programs, Food MicroModel and Pathogen Modelling Program. In unprocessed fish there was only a 2 log increase in type E cell count at the time the toxicity first occurred after 2 weeks storage at 8 degrees C. Neither growth or toxin production was observed in raw pickled fish with a NaCl concentration of 6.7% (w/v) during 6 weeks storage at 6 degrees C. In cold-smoked fish with a NaCl level of 3.2% (w/v) toxic samples were detected after 3 and 4 weeks storage at 8 degrees C and 4 degrees C, respectively, without any increase in type E count. Both models were hampered by limitations to controlling environmental factors set by the programs which also had an adverse effect on the reliability of predictions. Most predictions generated by the models were inconsistent with the results observed in the challenge studies. In certain situations, the models seemed to be 'fail-safe', in that, the growth rate predicted from the model was faster or a predicted time to toxicity shorter than that which actually occurred in the food. In other situations, the predictions showed the product to be safe when it was not. The results demonstrate the need for further development and rigorous validation of the models before they are accepted for wider use by inspecting officials and the food industry.

Animals↗
PubMed2002

Structured model to predict intracellular amino acid shortages during recombinant protein overexpression in E. coli.

Recombinant protein overexpression and the classical stringent response have been shown to induce the same proteases. Since the stringent response was the result of an intracellular amino acid shortage, it was hypothesized that the overexpression of the recombinant protein also caused an intracellular amino acid shortage. A structured non-segregated kinetic mathematical model of recombinant Escherichia coli was developed to predict intracellular amino acid shortages during recombinant protein overexpression, and thus the induction of the stringent response. Two model recombinant proteins were examined, chloramphenicol acetyl-transferase (CAT) and an 'average protein'. The model predicted an aromatic amino acid shortage during CAT overexpression, as predicted based on the CAT's amino acid content. The model also predicted a shortage of the intracellular alanine family amino acid pool during CAT overexpression. This was unexpected due to the relatively low content of alanine family amino acids in CAT compared to the average E. coli protein. The model predicted alanine, glutarate, and aspartate family amino acid shortages during recombinant 'average protein' overexpression. Additionally, the model predicted a decrease in the ribosome pool at induction for both recombinant proteins, which agrees with published experimental results. Thus, the structured kinetic model was able to predict amino acid shortages, that could potentially cause a stringent response and elevated protease activity.

Amino Acids↗
PubMed2003

Selecting methods for the prediction of future events in cost-effectiveness models: a decision-framework and example from the cardiovascular field.

Evidence on the cost-effectiveness of healthcare interventions is increasingly required by decision-makers. Economic models can provide timely information on the long-term impact of new technologies. However, models have been criticised because of the implicit assumptions they make, in particular the methods used to extrapolate data are rarely documented. This paper presents a systematic process for choosing a method of predicting events in economic models. This process is illustrated using a model examining the cost-effectiveness of a new HMG-CoA reductase inhibitor (statin) for primary prevention of cardiovascular disease (CVD). The prediction of future CVD events is a central component of the model, and the choice of method for predicting events was an important issue in the model's development. A literature review identified 11 studies with the information required to predict CVD events. A set of criteria were developed to assess the different methods of risk estimation, covering issues like scientific validity and acceptability to decision-makers. Risk equations derived from the Framingham Heart Study were found to be most suitable for predicting future events in the economic model. The paper illustrates how the development of economic models can be made more transparent, and suggests that the process outlined may be applied to other disease areas where there are several event prediction methods to choose from. In disease areas where published methods for predicting events are not available, the process outlined can make the uncertainty this leads to explicit, and highlight where further research is required. Such transparency can help decision-makers understand the scientific basis underpinning models, and therefore make these models more acceptable and useful for health policy-making.

Cardiovascular Diseases↗
PubMed2004

Predicting capacities required in cardiology units for heart failure patients via simulation.

The management of a department of cardiology has to plan the capacity of both elective and non-elective patients. Heart failure (HF) patients are admitted to the hospital in a non-elective way. The precision with which the capacity needed for non-elective patients can be predicted determines the degree of flexibility in planning the admission of elective patients. In this study we want to determine how accurately we can predict the bed occupancy of HF patients using a simulation model. Data of the year 2000 were used to obtain the necessary probability distribution functions. Data from the year 2001 were used for determining the prediction accuracy. The results show that the arrival of new HF patients can be adequately predicted. However, the bed occupancy by new and especially current patients is predicted less accurately. Still in 70% (90%) of the days of a 5-day-prediction interval the error is at most one (two) bed(s). The results may improve if the cardiologist is asked to predict the length of stay of the current patients.

Bed Occupancy↗

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