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PubMed1999

Performance of a predictive model to identify undiagnosed diabetes in a health care setting.

OBJECTIVE: To develop a predictive model to identify individuals with an increased risk for undiagnosed diabetes, allowing for the availability of information within the health care system. RESEARCH DESIGN AND METHODS: A sample of participants from the Rotterdam Study (n = 1,016), aged 55-75 years, not known to have diabetes completed a questionnaire on diabetes-related symptoms and risk factors and underwent a glucose tolerance test. Predictive models were developed using stepwise logistic regression analyses with the absence or presence of newly diagnosed diabetes as the dependent variable and various items with a plausible connection to diabetes as the independent variables. The models were evaluated in another Dutch population-based study, the Hoorn Study (n = 2,364), in which the participants were aged 50-74 years. Performances of the predictive models were compared by using receiver-operator characteristics (ROC) curves. RESULTS: We developed three predictive models (PMs), PM1 contained information routinely collected by the general practitioner, while PM2 also contained variables obtainable by additional questions. The third predictive model, PM3, included variables that had to be obtained from a physical examination. These latter variables did not have additive predictive value, resulting in a PM3 similar to PM2. The area under the ROC curve was higher for PM2 than for PM1, but the 95% Cls overlapped (0.74 [0.70-0.78] and 0.68 [0.64-0.72], respectively). CONCLUSIONS: Using only information normally present in the files of a general practitioner, a predictive model was developed that performed similarly to one supplemented by information obtained from additional questions. The simplicity of PM1 makes it easy to implement in the current health care setting.

Aged↗

PubMed2006

External validation of outcome prediction model for ureteral/renal calculi.

PURPOSE: We externally validated a previously designed neural network model to predict outcome and duration of passage for ureteral/renal calculi. The model was also evaluated using a 6 mm largest stone dimension cutoff in predicting stone outcome. MATERIALS AND METHODS: The model was previously designed on 301 patients at Albany Medical Center (free shareware from www.uroengineering.com). The model had a prediction accuracy of 86% for passage outcome and 87% for passage duration. In this study we tested the model on a separate 384 patients from 6 different external institutions to assess the prediction accuracy. All patients had a single renal/ureteral calculus by evaluation in an emergency room setting or by primary physicians and were then referred for further treatment. Model accuracy was also compared to using a 6 mm largest stone dimension cutoff in predicting the need for intervention. RESULTS: Testing on the 384 patients from all 6 external institutions revealed an outcome prediction accuracy of 88%. The area under the ROC curve was 0.9. Using a 6 mm stone size cutoff provided 79% (ROC 0.8) accuracy. The model duration of passage prediction accuracy was 80% (133 patients passed the stone, area under ROC of 0.8). CONCLUSIONS: The model provided high stone outcome prediction accuracy (ROC of 0.9 and 0.8) at the 6 external institutions, comparable to that of the design institution. The model provided higher accuracy than using only the largest stone dimension as a cutoff. Increasing experience will further assess the model's accuracy.

Adolescent↗

PubMed2001

Premature cessation of breastfeeding in infants: development and evaluation of a predictive model in two Argentinian cohorts: the CLACYD study, 1993-1999. Córdoba Lactation, Feeding, Growth and Development study.

UNLABELLED: The objective of this study was to develop a model to predict premature cessation of breastfeeding of newborns, in order to detect at-risk groups that would benefit from special assistance programmes. The model was constructed using 700 children with a birthweight of 2000 g or more, in 2 representative cohorts in 1993 and 1995 (CLACYD I sample) in Córdoba, Argentina. Data were analysed from 632 of the cases. Mothers were selected during hospital admittance for childbirth and interviewed in their homes at 1 mo and 6 mo. To evaluate the model, an additional sample with similar characteristics was drawn during 1998 (CLACYD II sample). A questionnaire was administered to 347 mothers during the first 24-48 h after birth and a follow-up was completed at 6 mo, with weaning information on 291 cases. Premature cessation of breastfeeding was considered when it occurred prior to 6 mo. A logistic regression model was fitted to predict premature end of breastfeeding, and was applied to the CLACYD II sample. The calibration (Hosmer-Lemeshow C statistic) and the discrimination [area under the receiver operating characteristics (ROC) curve] of the model were evaluated. The predictive factors of premature end of breastfeeding were: mother breastfed for less than 6 mo [odds ratio (OR) = 1.84, 95% confidence interval (CI) 1.26-2.70], breastfeeding of previous child for less than 6 mo (OR = 4.01, 95% CI 2.58-6.20), the condition of the firstborn child (OR = 2.75, 95% CI 1.79-4.21), the first mother-child contact occurring after 90 min of life (OR = 1.88; 95% CI 1.22-2.91) and having an unplanned pregnancy (OR = 1.50, 95% CI 1.05-2.15). The calibration of the model was acceptable in the CLACYD I sample (p = 0.54), as well as in the CLACYD II sample (p = 0.18). The areas under the ROC curve were 0.72 and 0.68, respectively. CONCLUSION: A model has been suggested that provides some insight onto background factors for the premature end of breastfeeding. Although some limitations prevent its general use at a population level, it may be a useful tool in the identification of women with a high probability of early weaning.

Argentina↗

PubMed2003

A prediction model of 1-year mortality for acute ischemic stroke patients.

OBJECTIVE: To develop a prediction model for 1-year mortality in patients with acute ischemic stroke, with the model to be at least as useful and accurate as other previously developed prediction models. DESIGN: Retrospective cohort study. SETTING: Neurology department at an Australian tertiary teaching hospital. PARTICIPANTS: Four hundred forty consecutive patients diagnosed with acute ischemic stroke between July 1, 1995, and June 30, 1997. INTERVENTIONS: Two hundred twenty-three (51%) patients were randomly assigned to the derivation sample to develop a prediction model using the Cox proportional hazards model. The model was then validated in a validation sample of 217 (49%) patients. MAIN OUTCOME MEASURE: One-year mortality. RESULTS: Eight clinical predictors were included in the final model: unconsciousness (3 points), dysphagia (7 points), urinary incontinence (9 points), both sides affected (4 points), hyperthermia (4 points), ischemic heart disease (3 points), peripheral vascular disease (3 points), and diabetes mellitus (2 points). Patients with scores of 10 or higher were allocated to the high-risk group, which had a 1-year mortality rate of 76%, compared with a 1-year mortality rate of 8% in the low-risk group. There was no statistically significant difference in terms of sensitivity, specificity, and positive predictive value in the validation sample. CONCLUSION: We developed a predictive model for 1-year mortality in acute ischemic stroke patients. The model is easy to use and is comparable in its accuracy with other predictive models.

Aged↗

PubMed2005

Adjuvant radiotherapy is associated with increased sexual dysfunction in male patients undergoing resection for rectal cancer: a predictive model.

OBJECTIVES: The objectives of this study were to evaluate the effect of radiotherapy (RT) on sexual function in patients undergoing oncologic resection for rectal cancer, and to develop a mathematical model for quantifying the risk of sexual dysfunction through time for this group of patients. METHODS: Data were prospectively collected on patients undergoing proctosigmoidectomy (group 1: n = 101) or adjuvant radiotherapy (40-50 Gy) and resection (group 2: n = 100) for rectal cancer at a tertiary referral center between December 1998 and July 2004. Study end points were recorded at 7 time intervals (preoperatively, 4 months, 8 months, 1 year, 2 years, 3 years, and 4 years after surgery) and included: 1) ability to have an erection, 2) maintain an erection, 3) attain orgasm, 4) dry orgasm, and 5) whether they were sexually active. Multilevel logistic regression analysis for repeated measures was used to identify factors associated with the sexual dysfunction. A predictive model was developed and internally validated by comparing observed and model-predicted outcomes. RESULTS: Radiotherapy had an adverse effect on the ability to get an erection, maintain an erection, attain orgasm, and being sexually active in comparison with patients undergoing surgery alone (7.4%, 12.6%, 16.2%, and 13.7% reduction 8 months after surgery respectively; P < 0.05). The effect of sexual dysfunction deteriorated with age (odds ratio for erectile function, 0.40 per 10-year increase in age; 95% confidence interval, 0.29-0.49; P < 0.001). A significant variability in sexual function was present among the 7 time points with a maximal deterioration occurring at 8 months after surgery with subsequent slow but not complete recovery (P < 0.001). The predictive model showed adequate discrimination on 4 of the 5 domains of sexual dysfunction (area under the receiver operating characteristic curve >0.70). CONCLUSIONS: Radiotherapy has an adverse effect on sexual function, the effect being maximal at 8 months after surgery. The risk of sexual dysfunction can be quantified preoperatively using the proposed index and can assist patients in making better informed choices on the type of treatment they receive.

Aged↗

PubMedDate not supplied

Multifactorial analysis of local recurrences in rectal cancer, including DNA ploidy studies: a predictive model.

DNA ploidy studies were performed in 188 patients operated on for rectal cancer. In order to define different risk groups of patients, a stepwise logistic regression was carried out in 138 patients who underwent abdominal "curative" resections. Thirty-seven variables were analyzed. Although several variables were significant, only three improved the prognostic value: (1) more than three positive lymph nodes (p = 0.0007); (2) macroscopic local tumor invasion (p = 0.01); and (3) DNA ploidy (p = 0.03). Standardized discriminant coefficients were used to obtain a model and format for predicting local recurrences. This is the first time that a predictive model for rectal cancer, using DNA ploidy as a variable, is reported. Based on calculated discriminant values (DV), patients can be divided into three subgroups: (1) low risk for local recurrences (DV < -1.9, n = 56)--local recurrences were observed in two patients (3.6%); (2) moderate risk (DV between -1.9 and -0.6, n = 55)--local recurrences occurred in nine patients (16.4%); and (3) high risk (DV > -0.6, n = 27)--local recurrences occurred in 14 patients (51.8%). This predictive model for local recurrences has much better prognostic value than Dukes' staging (p < 0.0001).

Adult↗

PubMed2026

Externally validated risk prediction models for gestational diabetes mellitus: A systematic review and meta-analysis.

INTRODUCTION: Risk prediction models for gestational diabetes mellitus (GDM) offer potential for early identification and targeted prevention. External validation is crucial to assess model performance across diverse populations. Despite the availability of numerous GDM prediction models, limited evidence exists on their external validation frequency, methodological quality, and clinical applicability. This systematic review evaluated externally validated GDM prediction models, focusing on methodological rigor, reporting standards, and clinical relevance to inform future research and implementation. MATERIAL AND METHODS: Databases including Ovid MEDLINE, Embase, Scopus, Emcare, and CINAHL were searched up to May 1, 2025. Studies reporting external validation of GDM risk prediction models were included. Two reviewers independently screened studies. Data were extracted using the CHARMS framework, and risk of bias and applicability were assessed using PROBAST+AI. The study protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD420251125758). RESULTS: Twenty-six studies validated 33 models, with validation sample sizes ranging from 50 to 75 161. Over half used the IADPSG criteria to define GDM. Discrimination metrics were commonly reported, but calibration, overall performance, and clinical utility were often lacking. Meta-analysis was feasible for only four models: Teede et al., Nanda et al., Naylor et al., and Van Leeuwen et al., each showing fair discrimination. The Teede et al. model was the most widely validated, with 11 external validations across six continents and a pooled AUC of 0.72 (95% CI: 0.67-0.76). Despite fewer validations, the Nanda et al. model achieved the highest pooled discrimination (5 validations; pooled AUC 0.77, 95% CI: 0.74-0.80). The Naylor et al. and van Leeuwen et al. models also underwent meta-analysis, as sufficient external validation studies were available to support comparative performance assessment. Notably, 69.23% of studies had a high risk of bias. CONCLUSIONS: While many models showed acceptable predictive performance, most validations were methodologically weak. Future studies should follow best-practice guidelines and promote scalable validation strategies, such as algorithm sharing, to enhance clinical utility.

Humans↗

PubMed2001

Predictive modeling of the shelf life of fish under nonisothermal conditions.

The behavior of the natural microflora of Mediterannean gilt-head seabream (Sparus aurata) was monitored during aerobic storage at different isothermal conditions from 0 to 15 degrees C. The growth data of pseudomonads, established as the specific spoilage organisms of aerobically stored gilt-head seabream, combined with data from previously published experiments, were used to model the effect of temperature on pseudomonad growth using a Belehradek type model. The nominal minimum temperature parameters of the Belehradek model (T(min)) for the maximum specific growth rate (micro(max)) and the lag phase (t(Lag)) were determined to be -11.8 and -12.8 degrees C, respectively. The applicability of the model in predicting pseudomonad growth on fish at fluctuating temperatures was evaluated by comparing predictions with observed growth in experiments under dynamic conditions. Temperature scenarios designed in the laboratory and simulation of real temperature profiles observed in the fish chill chain were used. Bias and accuracy factors were used as comparison indices and ranged from 0.91 to 1.17 and from 1.11 to 1.17, respectively. The average percent difference between shelf life predicted based on pseudomonad growth and shelf life experimentally determined by sensory analysis for all temperature profiles tested was 5.8%, indicating that the model is able to predict accurately fish quality in real-world conditions.

Aerobiosis↗

PubMed2005

A predictive model for distinguishing ischemic from non-ischemic cardiomyopathy.

OBJECTIVES: To develop a predictive model to distinguish ischemic from non-ischemic cardiomyopathy MATERIAL AND METHOD: The authors randomly assigned 137 patients with LV systolic dysfunction into two subsets--one to derive a predictive model and the other to validate it. Clinical, electrocardiographic and echocardiographic data were interpreted by blinded investigators to the subsequent coronary angiogram results. Ischemic cardiomyopathy was diagnosed by the presence of significant coronary artery disease from the coronary angiogram. The final model had been derived from the clinical data and was validated using the validating set. The receiver-operating characteristics (ROC) curves and the diagnostic performances of the model were estimated. RESULTS: The authors developed the following model: Predictive score = (3 x presence of diabetes mellitus) + number of ECG leads with abnormal Q waves--(5 x presence of echocardiographic characteristic of nonischemic cardiomyopathy). The model was well discriminated (area under ROC curve = 0.94). Performance in the validating sample was equally good (area under ROC curve = 0.89). When a cut-off point > or = 0 was used to predict the presence of significant coronary artery disease, the model had a sensitivity, specificity and positive and negative predictive values of 100%, 57%, 74% and 100%, respectively. CONCLUSION: With the high negative value of this model, it would be useful for use as a screening tool to exclude non-ischemic cardiomyopathy in heart failure patients and may avoid unnecessary coronary angiograms.

Cardiomyopathies↗

PubMed1999

History and physical examination to estimate the risk of ectopic pregnancy: validation of a clinical prediction model.

STUDY OBJECTIVE: To prospectively validate a clinical prediction model for ectopic pregnancy (EP). METHODS: Prospective cohort with 14-month derivation and 12-month validation phases. All hemodynamically stable, first-trimester patients with abdominal pain or vaginal bleeding who presented to a military teaching hospital emergency department underwent follow-up until an outcome of intrauterine pregnancy (IUP) or EP was established. Patients were separated into the high-risk group, defined as having either peritoneal signs or definite cervical motion tenderness; intermediate-risk group, defined as the presence of pain or tenderness, other than midline cramping, plus absence of fetal heart tones, and absence of tissue visible at the cervical os; and low-risk group (neither high- nor intermediate-risk) using recursive partitioning. RESULTS: Summarizing both phases, 915 patients had 845 (93%) IUPs and 70 (7.6%) EPs, with 18 (1.9%) lost to follow-up. The clinical prediction model classified 75 (8.2%) into the high-risk group (sensitivity 31%, 95% confidence interval [CI] 21% to 44%; specificity 94%, 95% CI 92% to 95%); and 644 (70%) in the intermediate-risk group (sensitivity 98%, 95% CI 89% to 100%; specificity 25%, 95% CI 22% to 29%). The remaining 196 (21%) patients who met neither high-risk nor intermediate-risk criteria were classified into the low-risk group. On the basis of EP prevalence of 7.7%, the risk of EP was less than 1% (95% CI 0% to 3%) for the low-risk group, 7% (95% CI 5% to 10%) for the intermediate-risk group, and 29% (95% CI 19% to 41%) for the high-risk group. CONCLUSION: This clinical prediction model is useful for estimating the risk of EP in first-trimester patients, particularly when ancillary testing is equivocal or not readily available.

Adult↗

PubMed2002

Customized prediction models based on APACHE II and SAPS II scores in patients with prolonged length of stay in the ICU.

OBJECTIVE: To study customized APACHE II and SAPS II models in predicting hospital death in patients with a prolonged length of stay in the ICU. DESIGN: Prospectively collected database. SETTING: Thirteen ICUs with 5-10 beds in Finnish secondary referral hospitals. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The database was collected between 1994 and 1999 and included 23,953 ICU admissions. In order to customize the original APACHE II and SAPS II models and to validate the models, the database was randomly divided into customization data ( n=12,064) and into validation data ( n=11,889). Logistic regression analysis was used for customization. As the length of the ICU stay was prolonged, the calibration and discrimination of both customized models worsened gradually in the validation data. Patients whose ICU stay lasted 7 days or longer (1,312 patients) consumed more than one half of all ICU days and TISS-points. Among these patients, goodness-of-fit statistics was 221.5 and 306.3 ( P<0.0001 for both) and the areas under ROC curve 0.65 and 0.62 for the customized APACHE and SAPS models, respectively. The models underestimated the risk of death in the low range and overestimated it in the high range of predicted mortality. On the other hand, both models discriminated well between survivors and non-survivors if the ICU stay was 2 days or less. CONCLUSIONS: Despite customization, the predictive models may not support clinical decision-making in those patients who require a high share of resources. More relevant instruments are needed for the prediction of outcome of patient groups who consume the major part of ICU resources.

APACHE↗

PubMed2004

Well-conditioned model predictive control.

Model-based predictive control is an advanced control strategy that uses a move suppression factor or constrained optimization methods for achieving satisfactory closed-loop dynamic responses of complex systems. While these approaches are suitable for many processes, they are formulated on the selection of certain parameters that are ambiguous and also computationally demanding which makes them less suited for tight control of fast processes. In this paper, a new dynamic matrix control (DMC) algorithm is proposed that reduces inherent ill-conditioning by allowing the process prediction time step to exceed the control time step. The main feature, that stands in contrast with current DMC approaches, is that the original open-loop data are used to evaluate a "shifting factor" m in the controller matrix where m replaces the move suppression coefficient. The new control algorithm is practically demonstrated on a fast reacting process with better control being realized in comparison with DMC using move suppression. The algorithm also gives improved closed-loop responses for control simulations on a multivariable nonlinear process having variable dead-time, and on other models found in the literature. The shifting factor m is generic and can be effectively applied for any control horizon.

Journal Article↗

PubMed2001

HT29-MTX and Caco-2/TC7 monolayers as predictive models for human intestinal absorption: role of the mucus layer.

The permeability of 19 compounds in both the Caco-2/TC7 and HT29-MTX models was determined, and the ability of each model to predict intestinal absorption in humans was compared. Similar apparent permeability values (log P(app)) were obtained in both models for the majority of compounds tested, and plots of log P(app) versus fraction absorbed in humans gave comparable sigmoidal curves. A linear correlation was also observed between the log P(app) values derived from these two models, which suggests that HT29-MTX is an alternative model for absorption prediction in humans. The similarity of both the diffusion coefficients and permeability values obtained for a range of hydrophilic and lipophilic compounds in the two models indicates that the mucus layer secreted by the human adenocarcinoma HT29-MTX goblet cells does not constitute a diffusion barrier to such compounds. The lack of P-glycoprotein (P-gp) in the HT29-MTX cell line may explain the higher permeability values obtained for cimetidine and sumatriptan in this model compared with those derived from the Caco-2/TC7 monolayers. The results suggest that the HT29-MTX model can be used to rank order the passive permeability of compounds, irrespective of their potential interaction with P-gp, which may facilitate optimization of the physicochemical features of compounds within a chemical series.

Administration, Oral↗

PubMed1995

Predictive model to assess risk for cardiac allograft vasculopathy: an intravascular ultrasound study.

OBJECTIVES: This study was performed to assess the influence and interdependence of immunologic and nonimmunologic risk factors in the development of cardiac allograft vasculopathy. Another primary objective was to establish a clinically useful model for risk assessment of cardiac allograft vasculopathy that would facilitate identifying those heart transplant recipients likely to have severe intimal proliferation and thereby at greater risk for adverse clinical events. BACKGROUND: To our knowledge, no comprehensive intravascular ultrasound study has assessed the relative influences of both nonimmunologic and immunologic factors in the development of cardiac allograft vasculopathy, currently the major limitation to long-term cardiac allograft survival. METHODS: Using a computer-assisted model of stepwise logistic regression, immunologic and nonimmunologic risk factors were evaluated to help identify the development of severe intimal thickening in 101 subjects who underwent intravascular ultrasound. Prospective validation of the findings was performed in a separate consecutive cohort of 37 heart transplant recipients, and the accuracy of this model to predict a relative risk > 1 for the development of severe intimal hyperplasia was assessed. RESULTS: Significant independent predictors of severe intimal hyperplasia in this model included a donor age > 35 years, a first-year mean biopsy score > 1 (a measure not only of severity of rejection, but also of frequency of insidious rejection) and hypertriglyceridemia at two incremental levels of risk (150 to 250 mg/dl [1.70 to 2.83 mmol/liter] and > 250 mg/dl [2.83 mmol/liter]). Based on the absence (0) or presence (1) of these factors, 12 individual categories of risk were ascertained with increasing relative risks and predicted probabilities for severe intimal hyperplasia. Prospective validation of this model revealed a sensitivity and specificity of 70% and 90%, respectively, and the positive and negative predictive values were 85% and 80%, respectively. Additionally, subjects with severe intimal thickening had a four-fold higher cardiac event rate than those without severe intimal proliferation on intravascular ultrasound. CONCLUSIONS: This study establishes a clinically useful predictive model that can be applied to individual heart transplant recipients to assess their risk for developing significant cardiac allograft vasculopathy and, thus, aids in the identification of patients at risk for cardiac events in whom closer surveillance and risk factor modification may be warranted.

Adult↗

PubMed2005

Validation of a predictive model to estimate the risk of conversion from ocular hypertension to glaucoma.

OBJECTIVES: To develop and validate a predictive model to estimate the risk of conversion from ocular hypertension to glaucoma. METHODS: Predictive models for the 5-year risk of conversion to glaucoma were derived from the results of the Ocular Hypertension Treatment Study (OHTS). The performance of these models was assessed in an independent population of 126 subjects with ocular hypertension from a longitudinal study (Diagnostic Innovations in Glaucoma Study [DIGS]). The performance of the OHTS-derived models was assessed in the DIGS cohort according to equality of regression coefficients, discrimination (c-index), and calibration. RESULTS: Thirty-one patients (25%) developed glaucoma during follow-up. Hazard ratios for DIGS- and OHTS-derived predictive models were similar for age, intraocular pressure, central corneal thickness, vertical cup-disc ratio, and pattern standard deviation but were significantly different for the presence of diabetes mellitus. When applied to the DIGS population, the OHTS-derived predictive models had reasonably good discrimination (c-indexes of 0.68 [full model] and 0.73 [reduced model]) and calibration. CONCLUSIONS: The OHTS-derived predictive models performed well in assessing the risk of glaucoma development in an independent population of untreated subjects with ocular hypertension. A risk scoring system was developed that allows calculation of the 5-year risk of glaucoma development for an individual patient.

Disease Progression↗

PubMedDate not supplied

Mass gathering medicine: a predictive model for patient presentation and transport rates.

INTRODUCTION: This paper reports on research into the influence of environmental factors (including crowd size, temperature, humidity, and venue type) on the number of patients and the patient problems presenting to first-aid services at large, public events in Australia. Regression models were developed to predict rates of patient presentation and of transportation-to-a-hospital for future mass gatherings. OBJECTIVE: To develop a data set and predictive model that can be applied across venues and types of mass gathering events that is not venue or event specific. Data collected will allow informed event planning for future mass gatherings for which health care services are required. METHODS: Mass gatherings were defined as public events attended by in excess of 25,000 people. Over a period of 12 months, 201 mass gatherings attended by a combined audience in excess of 12 million people were surveyed throughout Australia. The survey was undertaken by St. John Ambulance Australia personnel. The researchers collected data on the incidence and type of patients presenting for treatment and on the environmental factors that may influence these presentations. A standard reporting format and definition of event geography was employed to overcome the event-specific nature of many previous surveys. RESULTS: There are 11,956 patients in the sample. The patient presentation rate across all event types was 0.992/1,000 attendees, and the transportation-to-hospital rate was 0.027/1,000 persons in attendance. The rates of patient presentations declined slightly as crowd sizes increased. The weather (particularly the relative humidity) was related positively to an increase in the rates of presentations. Other factors that influenced the number and type of patients presenting were the mobility of the crowd, the availability of alcohol, the event being enclosed by a boundary, and the number of patient-care personnel on duty. Three regression models were developed to predict presentation rates at future events. CONCLUSIONS: Several features of the event environment influence patient presentation rates, and that the prediction of patient load at these events is complex and multifactorial. The use of regression modeling and close attention to existing historical data for an event can improve planning and the provision of health care services at mass gatherings.

Anniversaries and Special Events↗

PubMed2006

Effect of misreported family history on Mendelian mutation prediction models.

People with familial history of disease often consult with genetic counselors about their chance of carrying mutations that increase disease risk. To aid them, genetic counselors use Mendelian models that predict whether the person carries deleterious mutations based on their reported family history. Such models rely on accurate reporting of each member's diagnosis and age of diagnosis, but this information may be inaccurate. Commonly encountered errors in family history can significantly distort predictions, and thus can alter the clinical management of people undergoing counseling, screening, or genetic testing. We derive general results about the distortion in the carrier probability estimate caused by misreported diagnoses in relatives. We show that the Bayes factor that channels all family history information has a convenient and intuitive interpretation. We focus on the ratio of the carrier odds given correct diagnosis versus given misreported diagnosis to measure the impact of errors. We derive the general form of this ratio and approximate it in realistic cases. Misreported age of diagnosis usually causes less distortion than misreported diagnosis. This is the first systematic quantitative assessment of the effect of misreported family history on mutation prediction. We apply the results to the BRCAPRO model, which predicts the risk of carrying a mutation in the breast and ovarian cancer genes BRCA1 and BRCA2.

Bayes Theorem↗

PubMed2003

Development and validation of a prediction model for strokes after coronary artery bypass grafting.

BACKGROUND: A prospective study of patients undergoing coronary artery bypass graft surgery (CABG) was conducted to identify patient and disease factors related to the development of a perioperative stroke. A preoperative risk prediction model was developed and validated based on regionally collected data. METHODS: We performed a regional observational study of 33,062 consecutive patients undergoing isolated CABG surgery in northern New England between 1992 and 2001. The regional stroke rate was 1.61% (532 strokes). We developed a preoperative stroke risk prediction model using logistic regression analysis, and validated the model using bootstrap resampling techniques. We assessed the model's fit, discrimination, and stability. RESULTS: The final regression model included the following variables: age, gender, presence of diabetes, presence of vascular disease, renal failure or creatinine greater than or equal to 2 mg/dL, ejection fraction less than 40%, and urgent or emergency. The model significantly predicted (chi(2) [14 d.f.] = 258.72, p < 0.0001) the occurrence of stroke. The correlation between the observed and expected strokes was 0.99. The risk prediction model discriminated well, with an area under the relative operating characteristic curve of 0.70 (95% CI, 0.67 to 0.72). In addition, the model had acceptable internal validity and stability as seen by bootstrap techniques. CONCLUSIONS: We developed a robust risk prediction model for stroke using seven readily obtainable preoperative variables. The risk prediction model performs well, and enables a clinician to estimate rapidly and accurately a CABG patient's preoperative risk of stroke.

Adult↗