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This paper summarises recent trends in predictive modelling of microbial lag phenomena. The lag phase is approached from both a qualitative and quantitative point of view. Major modelling approaches and experimental results are critically assessed. This review mainly focuses on the influence of temperature and culture history on the lag phase during growth of bacteria.
Progress in experimental and theoretical biology is likely to provide us with the opportunity to assemble detailed predictive models of mammalian cells. Using a functional format to describe the organization of mammalian cells, we describe current approaches for developing qualitative and quantitative models using data from a variety of experimental sources. Recent developments and applications of graph theory to biological networks are reviewed. The use of these qualitative models to identify the topology of regulatory motifs and functional modules is discussed. Cellular homeostasis and plasticity are interpreted within the framework of balance between regulatory motifs and interactions between modules. From this analysis we identify the need for detailed quantitative models on the basis of the representation of the chemistry underlying the cellular process. The use of deterministic, stochastic, and hybrid models to represent cellular processes is reviewed, and an initial integrated approach for the development of large-scale predictive models of a mammalian cell is presented.
Traditional regression analysis of body weight growth curves encounters problems when the data are extremely variable. While transformations are often employed to meet the criteria of the analysis, some transformations are inadequate for normalizing the data. Regression analysis also requires presuppositions regarding the model to be fit and the techniques to be used in the analysis. An alternative approach using artificial neural networks is presented which may be suitable for developing predictive models of growth. Neural networks are simulators of the processes that occur in the biological brain during the learning process. They are trained on the data, developing the necessary algorithms within their internal architecture, and produce a predictive model based on the learned facts. A dataset of Sprague-Dawley rat (Rattus norvegicus) weights is analyzed by both traditional regression analysis and neural network training. Predictions of body weight are made from both models. While both methods produce models that adequately predict the body weights, the neural network model is superior in that it combines accuracy and precision, being less influenced by longitudinal variability in the data. Thus, the neural network provides another tool for researchers to analyze growth curve data.
The objective of this study was to compare artificial neural network (ANN) and multivariable logistic regression analyses for prediction modeling of adverse outcome in pediatric meningococcal disease. We analyzed a previously constructed database of children younger than 20 years of age with meningococcal disease at four pediatric referral hospitals from 1985-1996. Patients were randomly divided into derivation and validation datasets. Adverse outcome was defined as death or limb amputation. ANN and multivariable logistic regression models were developed using the derivation set, and were tested on the validation set. Eight variables associated with adverse outcome in previous studies of meningococcal disease were considered in both the ANN and logistic regression analyses. Accuracies of these models were then compared. There were 381 patients with meningococcal disease in the database, of whom 50 had adverse outcomes. When applied to the validation data set, the sensitivities for both the ANN and logistic regressions models were 75% and the specificities were both 91%. There were no significant differences in any of the performance parameters between the two models. ANN analysis is an effective tool for developing prediction models for adverse outcome of meningococcal disease in children, and has similar accuracy as logistic regression modeling. With larger, more complete databases, and with advanced ANN algorithms, this technology may become increasingly useful for real-time prediction of patient outcome.
The purpose of this study was to examine the predictive factors of quality of life for inpatients with depressive disorders. Eighty-three patients (mean age 44; 73% female) with depressive disorders were recruited from the psychosomatic ward of a medical center in the northern part of Taiwan. The predictive models of this study were established by encompassing three constructs: clinical variables, demographics, and perceived competence. The outcome variables of this study included an overall quality of life score and four domains' scores of the World Health Organization Quality of Life-brief version (WHOQOL-BREF). Stepwise regression analysis was used to identify significant factors related to the outcome variables. The results showed that there were five distinct models for the various domains of the quality of life. The predictive variables of the final model for overall quality of life included: the Beck Anxiety Inventory, the Canadian Occupational Performance Measure-satisfaction, and the Occupational Self Assessment-self. For the physical domain of the quality of life model, the adjusted Beck Depression Inventory-II, the Beck Anxiety Inventory, and the Activity of Daily Living Inventory were the significant predictors. In the psychological domain, the adjusted Beck Depression Inventory-II and age were the predictive factors. The adjusted Beck Depression Inventory-II, the Beck Anxiety Inventory and the Occupational Self Assessment-environment were the predictors for the social domain of quality of life. Finally, the adjusted Beck Depression Inventory-II, age, and the Occupational Self Assessment-environment were the predictors for the environmental domain of quality of life. The significance of the perceived competence variables in the quality of life of patients with depression indicates that occupational therapy intervention is warranted.
The objective of this study was to develop an anthropometry-based prediction model for the assessment of bone mineral content (BMC) in children. Dual-energy X-ray absorptiometry (DXA) was used to measure whole-body BMC in a heterogeneous cohort of 982 healthy children, aged 5-18 years, from three ethnic groups (407 European- American [EA], 285 black, and 290 Mexican-American [MA]). The best model was based on log transformations of BMC and height, adjusted for age, gender, and ethnicity. The mean +/- SD for the measured/predicted in ratio was 1.000 +/- 0.017 for the calibration population. The model was verified in a second independent group of 588 healthy children (measured/predicted In ratio = 1.000 +/- 0.018). For clinical use, the ratio values were converted to a standardized Z score scale. The whole-body BMC status of 106 children with various diseases (42 cystic fibrosis [CF], 29 juvenile dermatomyositis [JDM], 15 liver disease [LD], 6 Rett syndrome [RS], and 14 human immunodeficiency virus [HIV]) was evaluated. Thirty-nine patients had Z scores less than -1.5, which suggest low bone mineral mass. Furthermore, 22 of these patients had severe abnormalities as indicated by Z scores less than -2.5. These preliminary findings indicate that the prediction model should prove useful in determining potential bone mineral deficits in individual pediatric patients.
The objective of the present study was to evaluate the validity of a recently developed extrapolation model for the prediction of concentrations of chemicals in serum which are equivalent to in vitro effective nominal concentrations. Necessary input data are in vitro toxic concentrations and distribution relevant system and substance specific parameters, e.g. lipid volume fractions and albumin concentrations, octanol/water partition coefficients and specific binding to albumin. It was investigated whether the influence of human and bovine serum, respectively, on nominal cytotoxic potencies (EC(50)-values) of selected chemicals in vitro can be properly predicted using this algorithm. Cytotoxicity was determined as growth inhibition of proliferating Balb/c 3T3 cells after exposure for 72 h. Concentration-effect relationships were measured in the presence of 2% foetal bovine serum (FBS) and, additionally, 18% FBS or human serum (HS), or 1% (w/v) bovine (BSA) or human (HSA) albumin, respectively. Addition of HSA and BSA increased the EC(50)-values of the different chemicals by factors of 2.1 - 22 and 1.7 - 29, respectively. From these measurements values for the specific binding of the test compounds to BSA and HSA were derived. Addition of 18% HS increased the EC(50)-values by factors between 4.2 and 52, while addition of 18% FBS resulted only in 1.5 - 10.4-fold increases. A comparison of experimentally determined and calculated EC(50)-values revealed that the differing influence of human and bovine serum was quite well predicted by the extrapolation model. Deviations did not exceed the factor 3 and were in most cases lower than 2. It is concluded that the extrapolation model is quite well suited to predict equivalent concentrations in serum from in vitro effective concentrations.
Delirium, a cognitive and behavioral disorder affecting more than one third of all hospitalized elderly patients, is often misdiagnosed or unrecognized by caregivers, leading to higher patient morbidity and mortality rates. Prediction of the disorder, based on known predisposing and precipitating risk factors, can be used to target susceptible patients for prevention and early intervention. Predictive models need to be evaluated for clinical application and predictive value. Therefore, in this study, four predictive models were applied on a case-by-case basis to an elderly sample of 10 delirious patients and 10 nondelirious patients to determine sensitivity, specificity, and predictive values in a critical care setting. Results indicated six individual significant variables in these models: age, infection, dementia, blood urea nitrogen-to-creatinine ratio, severe illness, and comorbidity. A final multivariate model, derived from all variables, exhibited a sensitivity of 100% and specificity of 90% in predicting delirium in this study. Further studies are needed to substantiate these results. Then, identified risk factors can be incorporated into delirium prevention protocols for use by nurses at the bedside.
Disease management programs have historically had difficulty demonstrating the financial value of their programs using statistically rigorous methods. This preliminary study utilized a predictive model built from a control group that consisted of individual employer groups whose benefits managers did not purchase disease management. The purpose of this evaluation was to examine the cost savings associated with Health Management Corporation's disease management program for asthma, diabetes, and coronary artery disease. Study and control group members were identified with exactly the same selection criteria as self-insured employer groups (n = 76,194) with preferred provider organization health plans that offered the disease management program. The study group consisted of members (n = 1,009) who were identified as having any of the three conditions and were eligible for the disease management program, regardless of the extent of their participation. The control group included members (n = 2,491) who were identified as having any of the same three conditions. The control group data were used to develop a predictive model designed to calculate expected medical claims costs for the study group. The gross savings of the disease management program was measured by calculating the difference between the expected medical claims costs predicted by the model and the actual medical claims costs for the study group. Preliminary analyses indicate that Health Management Corporation's disease management program produced a return on investment of $2.84: $1.00 and $1.45 gross savings per member per month.
OBJECTIVE: To evaluate the organ system failures hospital mortality predictions in critically ill patients with acute renal failure requiring dialysis. DESIGN: Prospective, cohort study. SETTING: Intensive care units in a tertiary care university hospital in Taiwan. PATIENTS: A total of 112 patients admitted to the intensive care units with acute renal failure who required dialysis from January 1999 through December 1999. INTERVENTIONS: Collection of information necessary to compute the number of failed organs. MEASUREMENTS AND RESULTS: Of the 112 patients studied, 75 were men and 37 were women. The mean age of survivors and non-survivors was 58.59 +/- 19.91 years and 58.76 +/- 19.62 years. The overall mortality rate was 67%. There were no significant differences between survivors and non-survivors in terms of age, gender, or indication for dialysis. The cause of death in the majority of patients was related to organ system failure during the 24 hours immediately preceding the initiation of acute hemodialysis, and carry mortality rates exceeding 83% with the coexistence of four or more failed organs. The area under the organ system failures prediction model receiver operating characteristic curve equaled 0.772 +/- 0.046. CONCLUSION: We conclude that mortality rate for acute renal failure in intensive care unit patients continues to be high. Organ system failures prediction model performed well and simple in its ability to identify patients who die in hospital. Mortality rate increases as number of failed organ increases.
BACKGROUND: Intensive care resources for the management of severe diffuse brain injury patients (SDBI) are limited. Their optimal use is possible only if we can predict at admission which patients are unlikely to improve. AIMS: To develop a simple and effective model to predict poor outcome in patients with SDBI in order to help guide initial therapy. MATERIAL AND METHODS: The prognostic factors and outcomes of 289 patients with severe diffuse brain injury (GCS 3-8) were analyzed retrospectively. The prognostic factors analyzed were age, mode of injury, GCS at admission, pupillary reaction, horizontal oculocephalic reflex, and CT scan findings. Outcome at 1 month was classified as unfavorable--death or persistent vegetative state, or favorable--improvement with or without some disability. A stepwise linear logistic regression analysis was used to identify the most important predictors of poor outcome. A prediction model (NIMHANS model-NM) was developed using these factors. NM and several currently available outcome prediction models were prospectively applied in a separate group of 26 patients with severe diffuse brain injury managed with a different protocol. RESULTS: The most important predictors of poor outcome were found to be the horizontal oculocephalic reflex, motor score of GCS, and midline shift on CT scan. NM was found to be more sensitive (75%) and specific (67%) than most other models in predicting unfavorable outcome. NM had high false pessimistic results (33%). CONCLUSION: Prediction models cannot be used to guide initial therapy.
OBJECTIVE: To determine how much of the variations of blood pressure during a 24 hour period could be accounted for by a change in activity and establish a predictive model. MATERIALS AND METHODS: Twenty three healthy subjects (mean age 25 +/- 2 years) were studied. The BP, heart rate (HR), and time of measure (T) were recorded by ambulatory BP monitoring using Spacelabs (4 measures per hour). At each measure the subject noted in a diary the degree of activity on a six level semi-quantitative scale. DATA ANALYSIS: A model was constructed using an analysis of covariance. Different parameters were added in succession to reach a model of the type P: P0 + A + beta + (HR-HR0) + H, were P = predicted systolic pressure, P0 = mean systolic BP over the 24 hours. A variation in systolic BP for activity level, beta = the slope of the regression between systolic BP and HR during activity A, and HR0 the mean HR during this activity. RESULTS: 1) In order to test the model, the values measured in one subject were compared to the predicted values from the model in 22 others. The procedure was then repeated for the other subjects. This common model predicted 41 +/- 21% of fluctuations in BP of the subject analysed with a range of 0 to 66%. 2) In order to refine the individual model two subjects were explored 7 times over 24 h of non consecutive days. The measures of the last recording were compared to the predicted values from the application of the model to the six preceding recordings. The model then predicted 81% and 66% of the BP values of the test day. The mean of the 24 hour individual difference over a one hour period between the measures and its predicted value by the model was 0.13 +/- 4.8 mmHg, and -0.75 +/- 7.7 mmHg. CONCLUSION: This study expresses in a quantitative fashion the importance of the level of activity in the evaluation of the level of ambulatory BP. The introduction of this method of quantification and analysis seems logical in therapeutic trial. The difference in the predictions by the model for some subjects poses the problem of uniform coding of activities and that of the recognition of other events such as stress and dreaming in sleep.
OBJECTIVE: To determine the validity of the GRACE (Global Registry of Acute Coronary Events) prediction model for death six months after discharge in all forms of acute coronary syndrome in an independent dataset of a community based cohort of patients with acute myocardial infarction (AMI). DESIGN: Independent validation study based on clinical data collected retrospectively for a clinical trial in a community based population and record linkage to administrative databases. SETTING: Study conducted among patients from the EFFECT (enhanced feedback for effective cardiac treatment) study from Ontario, Canada. PATIENTS: Randomly selected men and women hospitalised for AMI between 1999 and 2001. MAIN OUTCOME MEASURE: Discriminatory capacity and calibration of the GRACE prediction model for death within six months of hospital discharge in the contemporaneous EFFECT AMI study population. RESULTS: Post-discharge crude mortality at six months for the EFFECT study patients with AMI was 7.0%. The discriminatory capacity of the GRACE model was good overall (C statistic 0.80) and for patients with ST segment elevation AMI (STEMI) (0.81) and non-STEMI (0.78). Observed and predicted deaths corresponded well in each stratum of risk at six months, although the risk was underestimated by up to 30% in the higher range of scores among patients with non-STEMI. CONCLUSIONS: In an independent validation the GRACE risk model had good discriminatory capacity for predicting post-discharge death at six months and was generally well calibrated, suggesting that it is suitable for clinical use in general populations.
Predictions arising out of a mathematical model that describes the expansion of leukaemia from a diffusion-orientated perspective are critiqued and validated by employing available real time data. Based on agreements found between model predictions and the data, but mindful of the limitations it presents, it is concluded that the model could be used to describe the dynamics of normal and abnormal cells in leukaemia. It is suggested that further studies of the behaviour of certain normal cell types in contrast to abnormal cells during leukaemic development could engender additional insights into leukaemia and its treatment.
Following earlier papers that established the mathematical form of the time dependence of lactate concentrations during recovery from several types of exercise, and that set up a two-compartment model predicting the same time dependences, the present work applies the model to obtain parameters of specific physiological processes. Satisfactory agreement between predictions of the model and our experiment and literature data is obtained in the cases were comparisons can be made, as in the muscular lactate time evolution measured from biopsy samples, in blood flows through the active muscle at the end of exercise or at rest and their evolution during recovery, as well as in the volume of the active muscle compartment. The model prediction that lactate efflux from the muscles to the blood can reduce to zero during recovery is verified experimentally.
Experiments and model calculations were performed to study the influence of within-channel cues versus across-channel cues in comodulation masking release (CMR). A class of CMR experiments is considered that are characterized by a single (unmodulated or modulated) bandpass noise masker with variable bandwidth centered at the signal frequency. A modulation-filterbank model suggested by Dau et al. [J. Acoust. Soc. Am. 102, 2892-2905 (1997)] was employed to quantitatively predict the experimental data. Effects of varying masker bandwidth, center frequency, modulator bandwidth, modulator type, and signal duration on CMR were examined. In addition, the effect of band limiting the noise before or after modulation was shown to influence the CMR in the same way as a systematic variation of the modulation depth. It is demonstrated that a single-channel analysis, which analyzes only the information from one peripheral channel, quantitatively accounts for the CMR in most cases, indicating that an across-channel process is generally not necessary for simulating results from this class of CMR experiments. True across-channel processes may be found in another class of CMR experiments.
A number of new predictive modeling techniques have emerged in the past several years. These methods can be used independently or in combination with traditional modeling techniques to produce useful tools for the management of prostate cancer. Investigators should be aware of these techniques and avail themselves of their potentially useful properties. This review outlines selected predictive methods that can be used to develop models that may be useful to patients and clinicians for prostate cancer management.
OBJECTIVE: We sought to evaluate several statistical modeling approaches in predicting prospective total annual health costs (medical plus pharmacy) of health plan participants using Pharmacy Health Dimensions (PHD), a pharmacy claims-based risk index. METHODS: We undertook a 2-year (baseline year/follow-up year) longitudinal analysis of integrated medical and pharmacy claims. Included were plan participants younger than 65 years of age with continuous medical and pharmacy coverage (n = 344,832). PHD drug categories, age, gender, and pharmacy costs were derived across the baseline year. Annual total health costs were calculated for each plan participant in follow-up year. Models examined included ordinary least squares (OLS) regression, log-transformed OLS regression with smearing estimator, and 3 two-part models using OLS regression, log-OLS regression with smearing estimator, and generalized linear modeling (GLM), respectively. A 10% random sample was withheld for model validation, which was assessed via adjusted r, mean absolute prediction error, specificity, and positive predictive value. RESULTS: Most PHD drug categories were significant independent predictors of total costs. Among models tested, the OLS model had the lowest mean absolute prediction error and highest adjusted r. The log-OLS and 2-part log-OLS models did not predict costs accurately as the result of issues of log-scale heteroscedasticity. The 2-part model using GLM had lower adjusted r but similar performance in other assessment measures compared with the OLS or 2-part OLS models. CONCLUSION: The PHD system derived solely from pharmacy claims data can be used to predict future total health costs. Using PHD with a simple OLS model may provide similar predictive accuracy in comparison to more advanced econometric models.