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PubMed2006

Predictive modelling of temperature and water activity (solutes) on the in vitro radial growth of Botrytis cinerea Pers.

The objective of this work was to develop validated models predicting the 'in vitro' effect of a(w) and temperature on the radial growth of Botrytis cinerea. The growth rate (g, mm d(-1)) of B. cinerea was calculated at three incubation temperatures (25 degrees C, 15 degrees C, 5 degrees C) and six water activities (ranging from 0.995 to 0.890). The water activity was adjusted with glucose, NaCl, glycerol, or sorbitol. Statistical analysis showed a significant effect of temperature, solute, a(w), and their two- and three-way interactions on the growth rate. No growth was observed at a(w)=0.93 in the presence of NaCl or at 0.89 in the presence of a non-ionic solute. The maximum colony growth rate decreased when the incubation temperature and water activity was lowered. Secondary models, relating the colony growth rate with a(w) or a(w) and temperature were developed. Optimum a(w) values for growth ranged from 0.981 to 0.987 in glycerol-, sorbitol-, or glucose-modified medium and were close to 1 in NaCl-modified medium. A quadratic polynomial equation was used to describe the combined effects of temperature and a(w) on g (mm d(-1)) in the presence of each solute. The highest and lowest radial growth rates were observed in models based on glucose and NaCl respectively, whatever the incubation temperature. All models prove to be good predictors of the growth rates of B. cinerea within the limits of experiments. The quadratic polynomial equation has bias factors of 0.957, 1.036, 0.950, and 0.860 and accuracy factors of 1.089, 1.070, 1.120 and 1.260 in media supplemented with glucose, NaCl, glycerol and sorbitol respectively. The results from modelling confirm the general finding that a(w) has a greater influence on fungal growth than temperature.

Botrytis↗

PubMed2000

Predictive model for the growth of Yersinia enterocolitica under modified atmospheres.

A quadratic response surface model is presented to describe the maximum specific growth rate of Yersinia enterocolitica, at refrigeration temperatures, under modified atmospheres. The presence of CO2 affected mainly the lag phase of the organism. The length of the lag phase increased with higher levels of CO2 in the atmosphere, and this effect was more noticeable at low temperatures. The effect of oxygen was similar but less pronounced. The observed growth was slower with higher CO2. Oxygen also decreased the growth rate, but its effect was significant only when its proportion in the atmosphere was greater than about 40%. Model predictions were compared with growth rates obtained in sea food inoculated with Y. enterocolitica and packaged under modified atmospheres. Predictions were also checked to determine whether they were inside the strict interpolation region of the model.

Animals↗

PubMed2005

A predictive model for the development of hormone-responsive breast cancer.

BACKGROUND: Effective therapies to reduce the risk of hormone-sensitive breast cancers (ER or PR positive) exist. Available models predict the risk of breast cancer without addressing hormone receptor status. The purpose of this study was to identify risk factors predictive of the development of hormone-sensitive cancers. METHODS: A total of 1285 invasive breast cancers in 1263 women were identified from a prospectively maintained database. Risk factors were compared for ER+ and ER- cancers by using Fisher's exact test. RESULTS: Models were developed for premenopausal and postmenopausal women. In premenopausal women, white race, age at menarche < 12 years, and nulliparity or age at first birth > 20 years were used. The risk of ER+ cancer increased from 67.7% with 0 variables to 83.8% with all three (P = .013). In postmenopausal women, white race and a history of estrogen therapy were used. With none of the variables present, the incidence of ER+ cancer was 70.0%; it was 77.6% with one variable and 85.4% with both variables (P = .002). In postmenopausal women, variables predicted significant differences in hormone sensitivity only for those aged < or = 60 years. In the subset of women with information on alcohol use, adding this variable to the model improved the prediction of hormonal status. CONCLUSIONS: Our findings, if prospectively validated, may help identify those who would obtain the greatest benefit from hormonal chemoprevention strategies for breast cancer risk reduction.

Adult↗

PubMed1996

Predictive model of the effect of temperature, pH and sodium chloride on growth from spores of non-proteolytic Clostridium botulinum.

Non-proteolytic strains of Clostridium botulinum are capable of growth at chill temperatures and thus pose a potential hazard in minimally-processed chilled foods. The combined effect of pH (5.0-7.3), NaCl concentration (0.1-5.0%) and temperature (4-30 degrees C) on growth of non-proteolytic C. botulinum in laboratory media was studied. Growth curves at various combinations of pH, NaCl concentration and temperature were fitted by the Gompertz and Baranyi models, and parameters derived from the curve-fit were modelled. Predictions of growth from the models were compared with data in the literature and this showed them to be suitable for use with fish, meat and poultry products. This model should contribute to ensuring the safety of minimally-processed foods with respect to non-proteolytic C. botulinum.

Clostridium botulinum↗

PubMed1992

Cross-validation performance of mortality prediction models.

Mortality prediction models hold substantial promise as tools for patient management, quality assessment, and, perhaps, health care resource allocation planning. Yet relatively little is known about the predictive validity of these models. We report here a comparison of the cross-validation performance of seven statistical models of patient mortality: (1) ordinary-least-squares (OLS) regression predicting 0/1 death status six months after admission; (2) logistic regression; (3) Cox regression; (4-6) three unit-weight models derived from the logistic regression, and (7) a recursive partitioning classification technique (CART). We calculated the following performance statistics for each model in both a learning and test sample of patients, all of whom were drawn from a nationally representative sample of 2558 Medicare patients with acute myocardial infarction: overall accuracy in predicting six-month mortality, sensitivity and specificity rates, positive and negative predictive values, and per cent improvement in accuracy rates and error rates over model-free predictions (i.e., predictions that make no use of available independent variables). We developed ROC curves based on logistic regression, the best unit-weight model, the single best predictor variable, and a series of CART models generated by varying the misclassification cost specifications. In our sample, the models reduced model-free error rates at the patient level by 8-22 per cent in the test sample. We found that the performance of the logistic regression models was marginally superior to that of other models. The areas under the ROC curves for the best models ranged from 0.61 to 0.63. Overall predictive accuracy for the best models may be adequate to support activities such as quality assessment that involve aggregating over large groups of patients, but the extent to which these models may be appropriately applied to patient-level resource allocation planning is less clear.

Discriminant Analysis↗

PubMed2005

Predictive modelling and validation of Listeria innocua growth at superatmospheric oxygen and carbon dioxide concentrations.

The effect of superatmospheric oxygen and carbon dioxide concentrations on the growth of Listeria innocua, which was used as a model organism for the pathogen Listeria monocytogenes, was evaluated. The bacteria were grown on a nutrient agar surface at 7 degrees C. Three carbon dioxide levels (0%, 12.5% and 25%) were combined with different levels of high oxygen concentrations (above 20%) based on a mixture design. The applied oxygen concentrations did not significantly influence the growth. High CO2 concentrations, on the contrary, reduced the maximum specific growth rate and prolonged the lag time. An overall model to describe the growth of L. innocua under high carbon dioxide conditions was constructed based on nine growth experiments, using a weighted one-step regression procedure. The influence of carbon dioxide on lag time and maximum specific growth rate was described using Ratkowsky-type models and inserted in the Baranyi equation. The model described the growth very well. To assess the validity of the model, 14 additional experiments were carried out. There was a good correlation of the model predictions and observed validation data.

Carbon Dioxide↗

PubMed2005

Preoperative factors and models predicting mortality in liver transplantation.

We analyzed preoperative factors related to postoperative mortality after liver transplantation among a cohort of 268 consecutive liver transplant patients over 6 years. We studied the impact of 10 recipient variables, 14 donor features, and three operative aspects. We also studied the correlation with death and survival using various predictive scores (Child, Cordoba Score, MELD, and UCLA). Univariate analysis showed that the factors with a significant association with postoperative mortality were the use of noradrenaline in the donor, total ischemia time (>12 hours), and transplant indication (hepatitis C virus versus the rest). Multivariate analysis of mortality showed the impact of female donor sex, recipients over >60 years, recipient albumin less than 2.8, and total graft ischemia time more than 12 hours. Univariate analysis of 1-year survival showed a statistically significant relation with D/R gender similarity, as well as donor GOT (>170) and GPT (>140) values. Multivariate analysis of 1-year survival showed donor GOT (>170) and donor/recipient gender similarity to be significant. Concerning the prediction models, Child-Pugh (AB versus C) best determined postoperative mortality (P < .006), MELD was predictive of 1-year survival (P < .03). The most important variables related to postoperative mortality were total ischemia time over 12 hours, recipient albumin less than 2.8, and age above 60 years. The variable with most impact on 1-year survival was the degree of graft hepatocyte lesion as determined by GOT. The Child-Pugh system is still the best indicator of postoperative mortality, although MELD may also be a good predictor of survival.

Age Factors↗

PubMed2006

Development of pediatric comorbidity prediction model.

OBJECTIVE: To develop a comorbidity model for children that can be used with hospital discharge administrative databases. DESIGN: Retrospective study using administrative data obtained from the Canadian Institute for Health Information Discharge Abstract Database and the Deaths File to develop a logistic regression model. Hosmer-Lemeshow chi2 test was used to examine model fit. The C statistic was used to assess model discrimination. Bootstrapping was used to determine the stability of regression coefficients. SETTING: We used linked administrative databases to compile 339,077 hospital discharge abstracts from April 1, 1991, through March 31, 2002. PARTICIPANTS: Children between ages 1 and 14 years in Ontario, Canada. MAIN OUTCOME MEASURE: Death within 1 year of hospital discharge. RESULTS: The 27-variable pediatric comorbidity model predicted 1-year mortality with a C statistic of 0.83 in the Ontario data set from which it was derived. The presence of brain cancer (odds ratio, 76.38 [95% confidence interval, 53.40-109.27]) at hospital admission was the strongest predictor, followed by diabetes insipidus (odds ratio, 39.23 [95% confidence interval, 20.75-74.17]). CONCLUSION: Using clinical judgment and empirical modeling strategies, we were able to identify 27 diagnoses highly predictive of death for children between 1 and 14 years of age within 1 year of hospital discharge.

Adolescent↗

PubMed2001

Predicting models of outcome stratified by age after first stroke rehabilitation in Japan.

OBJECTIVE: A multivariate model predicting the function at discharge following inpatient rehabilitation has been previously produced. The aim of this study is to determine predictors of function at discharge for stroke outcome and examine their accuracy of prediction. DESIGN: Four hundred sixty-four stroke patients were enrolled. Sex, the nature of the stroke, age, onset to rehabilitation admission interval and length of rehabilitation hospital stay were obtained from their medical records. Patients were divided into the following five groups according to age: < or = 49, 50-59, 60-69, 70-79, and > or = 80 yr. Disability was assessed on admission and at discharge by the FIM. Stepwise multiple regression analysis was performed in each group. RESULTS: The model for patients aged 60-69 yr was best for accuracy of prediction and explained 76% of variation for discharge FIM total score. The equation: (expected discharge FIM total score) = 111.88 + 0.08 x (the type of stroke) - 0.11 x (age) + 0.81 x (admission FIM total score) - 0.12 x (onset to rehabilitation admission interval), R = 0.87, R2 = 0.76, P < 0.0001. The type of stroke = 1 for cerebral infarction and 0 otherwise. Length of rehabilitation stay is not selected as a predictor. CONCLUSION: The stratification of patients by age is useful to determine predictors of function at discharge for stroke outcome and to improve their accuracy of prediction.

Age Distribution↗

PubMed1992

A model predicting dentists' willingness to treat HIV-positive patients.

Data for this study of dentists' willingness to treat HIV-positive (HIV+) individuals were derived from a survey of a probability sample of American general practitioner dentists (GPD). Data were received from 1,351 active GPD, which represented an 88% response rate. Because the outcome measure--willingness to treat HIV+ patients--is dichotomous, i.e., yes/no, logistic regression was selected as the statistical technique to be used for the creation of a predictive model. Seventeen independent variables were initially considered. The final and most parsimonious model contains six independent variables, of which perceived safety in treating HIV+ patients has the most predictive power. Fear of consequences for the practice, if HIV+ patients were seen, was also a powerful predictor, with a sense of ethical responsibility and a past history of treating HIV+ patients also being important predictive variables. Knowledge level about transmission of HIV and concern about risks associated with treating homosexuals were also significant.

Attitude of Health Personnel↗

PubMed1991

Mortality prediction models in intensive care: acute physiology and chronic health evaluation II and mortality prediction model compared.

OBJECTIVE: To compare the Acute Physiology and Chronic Health Evaluation (APACHE II) score with the Mortality Prediction Model (MPM). DESIGN: A prospective study. SETTING: A nine-bed ICU in a 300-bed, nonteaching secondary hospital. PATIENTS: Three hundred thirty-two consecutive, unselected adults. MEASUREMENTS AND MAIN RESULTS: We found a good correlation between APACHE II and MPM; their performance expressed as area under the receiver operating characteristics curve was nearly the same. Goodness-of-fit between observed and expected occurrences was better for APACHE II than for admission MPM, which overestimated deaths. Because we evaluate patients early, often in the Emergency Department, we felt that a "lead-time bias" could explain this discrepancy. Reevaluation after initial stabilization improved the performance of the MPM model to the level of APACHE II. CONCLUSIONS: Our investigation indicates that both APACHE II and MPM are good predictors of hospital outcome in our population, but the level of intensive care services received before conventional ICU admission modifies accuracy of predictive models. In any study of outcome using comparative studies of classification systems, confounding biases should be measured.

Adult↗

PubMedDate not supplied

Psychosocial factors and health status in women with rheumatoid arthritis: predictive models.

INTRODUCTION: Health status, and consequently productivity and quality of life, depends on a multitude of factors. Numerous psychosocial factors have been associated with the concurrent health status of individuals with chronic disease. Previous studies have examined the relationship between singular psychosocial factors and health status in rheumatoid arthritis. This study evaluated the simultaneous interrelationships among selected psychosocial variable and health outcomes using data from a study of younger women diagnosed with rheumatoid arthritis (RA). METHODS: The hypothesized models were examined using data from a survey of 185 women with a mean age of 43 years, diagnosed with RA for an average of 6.6 years. Participants in the study completed the following measures: (1) Arthritis Impact Measurement Scales, (2) Multidimensional Pain Inventory, (3) Daily Hassles Scale, (4) Interpersonal Support Evaluation List, and (5) Perceived Self-Efficacy Scale. RESULTS: Using path analysis, the information provided by the LISREL program, and extant theory, two models were tested. The data provided support for all but two of the hypothesized relationships in the model predicting physical functioning. Pain severity and self-efficacy emerged as important variables in understanding individual variations in perceived physical functioning. In the second model, using perceived well-being as the outcome, two bidirectional relationships were noted: one between affective distress and social support, and the second between perceived well-being and daily stress. CONCLUSIONS: The models evaluated in this study support the provision of multifaceted interventions aimed at enhancing a woman's ability to manage her pain and stress while also enhancing her beliefs in her own abilities.

Activities of Daily Living↗

PubMedDate not supplied

Implementing a predictive modeling program, part II: Use of motivational interviewing in a predictive modeling program.

This is the second article of a two-part series about issues encountered in implementing a predictive modeling program. Part I looked at how to effectively implement a program and discussed helpful hints and lessons learned for case managers who are required to change their approach to patients. In Part II, we discuss the readiness to change model, examine the spirit of motivational interviewing and related techniques, and explore how motivational interviewing is different from more traditional interviewing and assessment methods.

Anger↗

PubMed2006

Modelling predictions of cancer deaths in Northern Ireland.

BACKGROUND: An ageing population has service planners concerned about future levels of disease which are age dependent. Predictions of mortality for colorectal, lung and breast cancers, which account for 30% of cancer cases and 40% of cancers deaths, were calculated for 2010 and 2015, based on trends in death rates and the predicted change in the demography of the Northern Ireland population. METHODS: The U.S. National Cancer Institute's "Joinpoint" program was used to check for structural breaks in the time series of cancer death rates from 1984 to 2004. The prediction models applied to the data allowed variations in trends across age groups to be taken into account. A linear model was used for increasing or constant trends and a log linear model was used where the trend was decreasing. The models assume the number of deaths in each stratum, defined by age-sex and time-period, is Poisson distributed, with the average value determined by a log or linear function. RESULTS: Recent trends in rates of cancers studied were downwards except for female lung. Predictions include decreased colorectal cancer deaths in females and lung cancer deaths in males. In females, lung cancer deaths are predicted to more than double by the year 2015 (473 deaths), based on the 1984 level. Colorectal death rates in males are predicted to drop, but the number of deaths will increase by more than 10%, due to demographic change. Numbers of breast cancer deaths are likely to rise slightly, despite falling age standardised death rates, due to an ageing population. CONCLUSIONS: This work has provided estimates of early future trends, useful to service planners, and highlights the need for tobacco control, to reduce numbers of lung cancer deaths in females. The recently announced control of environmental tobacco legislation is one welcome development which should reduce lung cancer mortality in Northern Ireland.

Adolescent↗

PubMed1999

A predictive model for the development of hepatocellular carcinoma, liver failure, or liver transplantation for patients presenting to clinic with chronic hepatitis C.

OBJECTIVE: Chronic infection with hepatitis C may lead to the development of cirrhosis, liver failure, and hepatocellular carcinoma. However, not all patients progress to these endpoints. Ideally, clinicians could improve their capability of stratifying the risk and the time frame within which their patients will progress to these endpoints. The purpose of the present study was to construct statistical models predicting disease progression for individual patients. METHODS: Study endpoints were the development of hepatocellular carcinoma, liver transplantation, or death due to liver disease. The study cohort was 256 patients with hepatitis C acquired from either blood transfusion or use of intravenous drugs. During follow-up, 17 patients developed hepatocellular carcinoma, seven received liver transplantation, and 12 died from liver disease. RESULTS: On multivariate analysis a history of decompensation (relative risk [RR] 4.321, 95% confidence interval [CI] 1.777-10.511) and the serum albumin (RR 0.253, 95% CI 0.136-0.474) were independently associated with the study endpoints. Patients without a history of decompensation and with a serum albumin > or = 4.1 mg/dl had a 3.2% chance of developing the study endpoints within 5 yr. Patients with a history of decompensation and a serum albumin < 4.1 mg/dl had a 40% chance of developing a study endpoint within 5 yr. Baseline genotype and quantitative RNA were not associated with development of the clinical endpoints, with the exception of patients coinfected with two or more genotypes. CONCLUSION: Thus, the serum albumin and a history of decompensation are useful for predicting the development of hepatocellular carcinoma, liver transplantation, and death due to liver disease among patients with hepatitis C.

Adult↗

PubMed1995

Validation of a bacteremia prediction model.

OBJECTIVE: To validate a previously published model for predicting bacteremia in hospitalized patients. DESIGN: Application of a published bacteremia prediction model to a prospective validation cohort of patients and comparison of its predictability to that found in the derivation cohort. SETTING: Urban, university-affiliated, 550-bed public hospital. PATIENTS: The validation cohort consisted of 342 patients with 559 blood culture episodes between October 14, 1992, and December 5, 1992. Each blood culture episode was scored based on the presence or absence of seven predictors of bacteremia and the findings compared with published results (derivation cohort). INTERVENTIONS: None. RESULTS: Application of the bacteremia prediction model to the validation cohort identified episodes with a low risk (3%) and a high risk (17%) for true bacteremia, similar to the findings in the derivation cohort (1% and 16%, respectively). Comparison of the predictions of the model in the two cohorts by receiver operator characteristic curve analysis revealed that the overall predictability of the model in the validation cohort was not as good as in the derivation cohort. CONCLUSIONS: Although the bacteremia prediction model did not perform as well overall in the validation cohort, the model still was able to clearly define two extreme groups: those with a low risk and those with a high risk for true bacteremia. This predictive capability may aid physicians in prescribing empiric antimicrobial therapy and also may be useful to hospital epidemiologists in assessing quality of care.

Adolescent↗

PubMed1991

Identifying patients at risk of becoming disabled because of low-back pain. The Vermont Rehabilitation Engineering Center predictive model.

A predictive risk model of low-back pain (LBP) disability was developed by a panel of six experts in the fields of chronic pain and disability. It comprised 28 factors organized into eight categories: job, psychosocial, injury, diagnostic, demographic, medical history, health behaviors, and anthropometric characteristics and was administered as a 15-minute written questionnaire. The model was tested prospectively on 250 patients (age range, 18-65 years) attending two secondary-care low-back clinics. Disability, as predicted by the model, was compared with 1) actual disability assessed 3 and 6 months later; 2) predictions of disability made by the attending physicians; and 3) predictions obtained from an empirically derived model. These results showed that 1) the expert-generated risk model had a predictive accuracy of 89% and did better in predicting disability than the physicians across all samples and 2) the empirically weighted model did best of all (91% predictive accuracy), suggesting that the expert model used appropriate factors but that the weights assigned to these factors by the panel of experts could be improved.

Adult↗

PubMed2006

A discriminative prediction model of neurological outcome for patients undergoing surgery of brain arteriovenous malformations.

BACKGROUND AND PURPOSE: To develop and validate a discriminative model for predicting neurological morbidity after brain arteriovenous malformation (bAVM) surgery. METHODS: Of 233 consecutive, prospectively enrolled patients undergoing bAVM surgery, the first 175 were used to derive, and the last 58 to validate, the prediction model. Demographic and angiographic factors were related to modified Rankin Scale scores assigned before, within 72 hours, at 7 days and at > or =1 year after surgery to seek predictors of postoperative neurological deficits (modified Rankin Scale score > or =3). These factors included nidus size, eloquence, venous drainage, diffuseness, white matter configuration, arterial perforator supply and associated aneurysms. RESULTS: Brain eloquence, diffuse nidus and deep venous drainage were significant predictors of early disabling neurological deficits (odds ratios of 4.33, 3.49 and 2.38, respectively). The rounded odds ratios form a weighted 9-point prediction model (maximum scores for eloquence+diffuseness+deep drainage=4+3+2). The score discriminated the probability of experiencing both early (first week) and permanently (at > or =1 year) disabling neurological deficits as follows: 0 to 2: 1.8%, 3 to 5: 17.4%, 6 to 7: 31.6%, >7: 52.9% for early and 0 to 2: 1.8%, 3 to 5: 4.4%, 6 to 7: 18.4%, >7: 32.4% for permanently disabling outcomes. The discrimination of the model was 0.80 with 2.8% optimism. Validation in the second patient cohort revealed good performance at risk stratification. CONCLUSIONS: Relative weights assigned to brain eloquence, diffuse nidus morphology and deep venous drainage of a bAVM provide a simple and discriminative prediction model for neurological outcome after bAVM surgery.

Adolescent↗