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PubMed2003

Predicting outcome in ischemic stroke: external validation of predictive risk models.

BACKGROUND: Six multivariable models predicting 3-month outcome of acute ischemic stroke have been developed and internally validated previously. The purpose of this study was to externally validate the previous models in an independent data set. SUMMARY OF REPORT: We predicted outcomes for 299 patients with ischemic stroke who received placebo in the National Institute of Neurological Disorders and Stroke rt-PA trial. The model equations used 6 acute clinical variables and head CT infarct volume at 1 week as independent variables and 3-month National Institutes of Health Stroke Scale, Barthel Index, and Glasgow Outcome Scale as dependent variables. Previously developed model equations were used to forecast excellent and devastating outcome for subjects in the placebo tissue plasminogen activator data set. Area under the receiver operator characteristic curve was used to measure discrimination, and calibration charts were used to measure calibration. The validation data set patients were more severely ill (National Institutes of Health Stroke Scale and infarct volume) than the model development subjects. Area under the receiver operator characteristic curves demonstrated remarkably little degradation in the validation data set and ranged from 0.75 to 0.89. Calibration curves showed fair to good calibration. CONCLUSIONS: Our models have demonstrated excellent discrimination and acceptable calibration in an external data set. Development and validation of improved models using variables that are all available acutely are necessary.

Aged↗

PubMed2002

A statistical model to predict nonsurvival in congenital diaphragmatic hernia.

OBJECTIVE: To develop a predictive model using echocardiographic indices to identify nonsurvivors from survivors in preoperative patients with congenital diaphragmatic hernia (CDH). STUDY DESIGN: Eight cases of CDH, with a mortality rate of 38%, underwent echocardiographic study before surgical repair. Left ventricular mass (LVMASS) using the area-length method of Wyatt et al. and fractional shortening (FS) by M-mode measurements were determined. RESULTS: We identified a nonlinear nonoverlapping distribution that predicted nonsurvivors from survivors, p=0.04. Multiple regression analysis demonstrated the quantity (LVMASS x FS)(1/2) to be correlated with nonsurvival with a coefficient of determination r(2)=0.55. Comparison of the means of the quantity (LVMASS x FS)(1/2) for the two groups suggested two distinct populations, p=0.04. CONCLUSION: The mathematical quantity (LVMASS x FS)(1/2) calculated from echocardiographic measurements obtained preoperatively in babies with CDH may predict nonsurvival despite maximal intervention.

Female↗

PubMed2026

Development and Validation of Machine Learning Models for Predicting Early Cognitive Decline Using Home Sensor-Derived Behavioral Data: Sensors in-Home for Elder Wellbeing (SINEW) Cohort Study.

BACKGROUND: As the global population continues to age, the prevalence of geriatric conditions, including dementia and frailty, is also increasing. Early identification of individuals at an elevated risk of these conditions, such as those presenting with mild cognitive impairment (MCI) or prefrailty, can provide a critical window for prompt intervention aimed at preventing or reversing disease progression. To promote such early identification, there is a burgeoning interest in the use of digital sensor technology and predictive modeling. OBJECTIVE: This study aimed to use a continuous, home-based monitoring sensor system for older adults to distinguish those exhibiting normal aging from those with MCI, early dementia, prefrailty, or frailty, and to predict their transition from normal aging to one of these conditions. METHODS: This longitudinal cohort study will recruit 200 community-dwelling adults aged ≥65 years with normal cognition or MCI at baseline. A multi-sensor system will be installed in participants' homes, including passive infrared motion sensors, door contact sensors, bed sensors, medication box sensors, wearable activity bands, and Bluetooth proximity beacons. These devices will continuously capture spatiotemporal activity patterns, mobility indicators, sleep behaviors, and medication-taking routines. Annual assessments will include standardized cognitive tests (eg, Montreal Cognitive Assessment, Mini-Mental State Examination, Rey Auditory-Verbal Learning Test, digit span, Color Trails Test, semantic fluency, Stroop), frailty measures (modified Fried phenotype, gait speed, grip strength), mental health scales, sleep quality, and psychosocial indicators. Sensor-derived features-such as gait variability, activity regularity, sleep fragmentation, and medication adherence patterns-will be integrated with clinical data to develop supervised machine learning models. Planned approaches include logistic regression, random forests, gradient boosting, and deep learning. Model performance will be evaluated using cross-validation and independent test sets. Primary metrics will include area under the receiver operating characteristic curve, sensitivity, specificity, precision, recall, and F1-score. Models will be benchmarked against gold-standard clinical diagnoses and validated using temporal subsets of the dataset. RESULTS: Enrollment for this study started in November 2019 and will continue until March 2030. As of June 2025, we have enrolled 138 participants. Full data analysis has yet to begin. CONCLUSIONS: We aim to develop a reliable and effective sensor system for in-home use that will facilitate the early detection of cognitive and physical decline. In so doing, it will add to our current understanding of digital biomarkers. It is common for older adults to seek clinical intervention only when their cognitive impairment has already reached an advanced stage. The implementation of readily deployable sensor systems within community settings presents us with opportunities for prompt intervention, which holds the potential for delaying or reversing disease progression and allowing for a greater number of functional and meaningful years.

Humans↗

PubMed2006

Cross-validation of a model for predicting functional status and length of stay in patients with stroke.

OBJECTIVE: In a study published in 2002, it was observed that a variable composed by the Functional Independence Measure (FIM) and the trunk control test at admission predicted 66.5% of the FIM at discharge in stroke patients. The objective was to confirm the reproducibility of this predictive model. METHODS: Retrospective study of 245 hemiparetic stroke inpatients of the rehabilitation department. The main variables studied were: trunk control test FIM at admission and compound variable (FIM+trunk control test) as independent variables and FIM at discharge and inpatient rehabilitation length of stay as dependent variables. RESULTS: Correlation between the compound variable and the length of stay was statistically significant (r=0.59), as was its correlation with the total FIM at discharge (r=0.82). The regression analysis predicted 34.3% of the length of stay variability and 66.4% of the total FIM at discharge variability. CONCLUSION: The compound variable is a reliable tool because of its reproducibility in predicting the functional level at hospital discharge in hemiparetic patients.

Activities of Daily Living↗

PubMed2006

Models for predicting type 1 diabetes in siblings of affected children.

OBJECTIVE: To generate predictive models for the assessment of risk of type 1 diabetes and age at diagnosis in siblings of children with newly diagnosed type 1 diabetes. RESEARCH DESIGN AND METHODS: Cox regression analysis was used to assess the risk of progression to type 1 diabetes, and multiple regression analysis was used to estimate the age at disease presentation in 701 siblings of affected children. Sociodemographic, genetic, and immunological variables were included in the analyses. Subanalyses were performed in a group of 77 autoantibody-positive siblings with additional metabolic data. RESULTS: A total of 47 siblings (6.7%) presented with type 1 diabetes during the 15-year observation period. Young age, an increasing number of detectable diabetes-associated autoantibodies at initial sampling and of affected first-degree relatives, and HLA DR-conferred disease susceptibility predicted progression to type 1 diabetes. In the subgroup of 77 autoantibody-positive siblings, young age, HLA DR-conferred susceptibility, an increasing number of autoantibodies, a reduced first-phase insulin response, and decreased insulin sensitivity in relation to first-phase insulin response were associated with increased risk of progression to type 1 diabetes. Age at diagnosis was predicted by age, insulinoma-associated protein 2 antibody levels, and number of autoantibodies at initial sampling (R(2) = 0.76; P < 0.001). In the smaller cohort of autoantibody-positive subjects, first-phase insulin response and HLA DR-conferred susceptibility were additional predictors of age at diagnosis. CONCLUSIONS: Information on autoantibody status and levels, HLA-conferred disease susceptibility, and insulin secretion and sensitivity seems to be useful in addition to age and family history of type 1 diabetes when assessing risk of progression to type 1 diabetes and time to diagnosis in siblings of children with newly diagnosed type 1 diabetes.

Adolescent↗

PubMedDate not supplied

Risk models for predicting chemotherapy-induced neutropenia.

Neutropenia and its complications, including febrile neutropenia, are major dose-limiting toxicities of systemic cancer chemotherapy. A number of studies have attempted to identify risk factors for neutropenia and its consequences to develop predictive models capable of identifying patients at greater risk for such complications and to guide more effective and cost-effective applications of the colony-stimulating factors. A systematic review of the literature showed that age, performance status, nutritional status, chemotherapy dose intensity, and low baseline blood cell counts were associated with the risk of severe and febrile neutropenia or reduced chemotherapy dose intensity in multivariate analysis in two or more studies. Similarly, age, diagnosis of leukemia or lymphoma, high temperature or low blood pressure at admission, and i.v. site infection along with low blood cell counts and organ dysfunction were associated with serious medical complications of febrile neutropenia, including bacteremia and death. The available risk model studies, however, had several limitations, including retrospective analyses of small study populations lacking independent validation, frequent missing values, and differences in the predictive factors considered. To overcome the limitations of previous studies, efforts are under way to develop and validate risk models based on large prospective studies in representative populations of patients receiving systemic chemotherapy.

Antineoplastic Agents↗

PubMed2026

Natural language processing-based model to predict radiation pneumonitis in patients with locally advanced non-small cell lung cancer undergoing chemoradiotherapy: a retrospective cohort study.

BACKGROUND: Radiation pneumonitis (RP) remains a significant treatment-related toxicity in patients with unresectable, locally advanced non-small cell lung cancer (NSCLC) undergoing chemoradiotherapy (CRT). Most existing predictive models rely on static baseline demographic or dosimetry variables and lack real-time clinical applicability. We developed a novel predictive framework that integrates longitudinal symptom data extracted from clinical notes using natural language processing (NLP) with clinical and dosimetry features to improve early RP prediction. METHODS: We retrospectively identified 227 patients with locally advanced NSCLC treated with definitive CRT at a high-volume cancer center in the United States. We included all patients older than 18 years who were diagnosed between Jan 1, 2006, and Dec 31, 2022 with histologically or cytologically confirmed unresectable Stage 2 or 3 NSCLC and treated with conformal radiotherapy to a minimum dose of ≥45 Gy with or without chemotherapy. Of these, 31 RP events were identified through manual adjudication using radiologic criteria and chart review. NLP was used to extract the temporal relationship of 16 pre-specified symptoms with treatment from over 100,000 clinical notes spanning pre- and during-treatment intervals. We trained and validated machine learning models on combinations of baseline clinical data, radiation dosimetry, and NLP-derived symptom features. Model performance was evaluated using a nested cross-validation framework, with an outer cross-validation loop reserved for performance assessment and an inner cross-validation loop used for model training and integration, and summarized using area under the receiver operating characteristic curve (AUC) and partial AUC (pAUC) at high specificity thresholds. Clinical utility was evaluated using decision curve analysis (DCA). FINDINGS: The best-performing model incorporated longitudinal NLP features and achieved a median AUC of 0.759 (90% confidence interval 0.753-0.766), significantly outperforming baseline models using only dosimetry (AUC 0.613) or clinical variables (AUC 0.635). NLP-based features such as cough trajectory, shortness of breath, and wheezing were among the most important predictors. Inclusion of NLP-derived symptom data improved early identification of high-risk patients, particularly in the clinically relevant high-specificity range (pAUC 0.021 vs. 0.010 for dosimetry alone). DCA showed that the calibrated MLP model provided greater net benefit than default strategies of treating all or no patients across clinically relevant threshold possibilities. INTERPRETATION: In this early work, NLP-based extraction of longitudinal symptoms from routine clinical documentation meaningfully enhances RP prediction in patients undergoing CRT for NSCLC. This approach leverages existing electronic health record infrastructure to deliver real-time, scalable, and interpretable risk estimates, offering a pathway toward potential early intervention and personalized toxicity management. The model and DCA requires external and prospective validation before clinical deployment; as such, future work should focus on this validation and integration into clinical decision support systems. FUNDING: AstraZeneca.

Chemoradiotherapy↗

PubMed1999

A model for predicting chronic TMD: practical application in clinical settings.

BACKGROUND: Substantial cost is associated with the treatment of chronic temporomandibular disorders, or TMDs, and patients with TMDs often experience significant psychosocial distress. Early intervention based on identified risk factors has potential financial and functional benefits. METHODS: Two hundred four patients with acute TMD were evaluated via an assessment battery that included physical, psychological and social measures. All participants were diagnosed as having TMD on the basis of the research diagnostic criteria for TMD, Axis I. At the six-month follow-up assessment, patients were considered to have chronic TMD if they continued to have TMD pain. This resulted in 144 of the patients being classified in the chronic group and 60 being classified in the nonchronic group. RESULTS: A comparison of the acute TMD data demonstrated that the group that went on to develop chronic TMD and the group that did not differed significantly in their scores on numerous biopsychosocial indexes. Although several biopsychosocial measures were found to differentiate these two groups before the onset of chronic TMD, logistic regression analysis demonstrated that a two-variable predictive model consisting of the presence of a muscle disorder and characteristic pain intensity (that is, the mean of these three ratings: patient's report of current pain, worst pain in the last three months and mean pain in the last three months) accurately classified 91 percent of the subjects who went on to develop chronic TMD. CONCLUSIONS: During the acute phase of TMD, two variables allowed for an accurate prediction rate of 91 percent among patients who went on to develop chronic TMD. CLINICAL IMPLICATIONS: This model provides clinicians with the opportunity to identify at-risk patients early and initiate adjunctive or alternative treatments, thus reducing the likelihood of the development of TMD chronicity.

Acute Disease↗

PubMed1989

Theoretical modeling to predict violence in hospitalized psychiatric patients.

Research from a nursing perspective on factors related to psychiatric inpatient violence is lacking. The purpose of the study was to test a nursing theoretical framework of violence using predictive model testing. Data were collected by 57 nursing staff on 162 hospitalized patients in four hospitals. Inconsistencies in the social and therapeutic rules predicted 18% of the violent behavior. Demographic and medical variables contributing to violence were: history of violence towards others, length of hospitalization, and the diagnosis of substance abuse. The findings support the need for further research to identify nursing concepts that contribute to patient violence.

Adult↗

PubMed1998

The influence of trend on estimates of accidents at junctions.

While reliable estimates of expected accidents can be achieved by combining observed accidents and accident model predictions using an empirical Bayes approach, there are a number of obstacles to the widespread adoption of the method. This paper concentrates on problems associated with the available predictive models. Of particular concern is the effect on model predictions of accident trends over time resulting from, for instance, traffic growth or national road safety programmes. Since accident models invariably include traffic flow as an explanatory variable, the effects of flow changes can be included provided that account is taken of the nonlinear relationship between accidents and exposure. It is, however, common to assume that accident risk per unit of exposure is constant over time, whereas national data imply that accident risk is declining. In addition, there is a need, in practice, to rank and evaluate remedial sites in terms of the specific accident types or severities which might be targeted by treatment (for example, wet road accidents in the case of surface treatment). This then raises the question of whether the proportions of accidents of various types varies over time or with traffic flow and site characteristics. Generalized linear modelling was used to develop regression estimates of expected junction accidents (both in total and disaggregated by severity, road surface condition and lighting condition) which allow for the possibility of accident risk varying over time. Accident risk at the sample of some 500 junctions was shown to be declining annually by an average of 6%, with no significant difference in the value of trend between accident types. The factors which affected the proportions of accidents of various types included the method of junction control, speed limit and traffic flow.

Accidents, Traffic↗

PubMedDate not supplied

Indoor air exposure to volatile compounds emitted by paints: experiment and model.

To describe the evaporation of organic solvents from paints and the resulting indoor concentrations, a mathematical model and an indoor paint experiment are presented. The model describes painting in terms of an increasing area of paint during application and two compartments of paint once applied. Evaporation of organic solvents is driven by the vapor pressure of the organic solvent. The experiment revealed concentrations of n-alkanes in indoor air, during painting, and 3 days thereafter. To compare experimental results to model predictions, model parameters were measured at the start of the experiment. Diffusional exchange between paint compartments and fraction of paint applied to the upper compartment were set by expert judgment. Model predictions and experimental results were in agreement, although the timing of the concentration peak appeared difficult to predict.

Air Pollutants↗

PubMed1992

[Application of a model for predicting morbidity in children from a low socioeconomic level].

An instrument to be used at a primary health care level was built on the basis of a predictive model for diarrhea obtained during a previous study. The instrument was applied to 720 mothers or caregivers of infants living in the Southeastern area of Santiago. 83 infants who fulfilled the requisites of the instrument were surveyed during July-August 1989 and compared to age, nutritional and socioeconomic status matched children not fulfilling the same requisites. Infants selected by the predictive instrument suffered 4 times more morbid episodes and respiratory infections and 5 times more diarrheal episodes than controls. Symptoms were present in them during 50% of the survey period as opposed to 14% in controls. Mothers of children at risk had inadequate behaviors in respect to health care of their children and did not follow many of the National Health programs available for their families. The predictive instrument tested may be useful to identify children at high risk of morbidity, creating the possibility for special interventions in them.

Child Welfare↗

PubMed1997

Lifetime smoking exposure affects the association of C-reactive protein with cardiovascular disease risk factors and subclinical disease in healthy elderly subjects.

Blood levels of C-reactive protein (CRP), a marker of inflammation, are related to cardiovascular disease risk. To determine cross-sectional correlates in the elderly, we measured CRP in 400 men and women older than 65 years and free of clinical cardiovascular disease at baseline as part of the Cardiovascular Health Study. Only 2% of the values were greater than 10 mg/L, the cut-point usually used to identify inflammation. CRP levels appeared tightly regulated, since there were strong bivariate correlations between CRP and the following: inflammation-sensitive proteins such as fibrinogen (r = .52); measures of fibrinolysis such as plasmin-antiplasmin complex (r = .23); pack-years of smoking (r = .30); and body mass index (r = .24; all P values < or = .001). The association with pack-years was independent of the length of time since cessation of smoking. CRP levels were also associated with coagulation factors VIIc, IXc, and Xc; HDL cholesterol (negative) and triglyceride; diabetes status; diuretic use; ECG abnormalities; and level of exercise. Because of effect modification, two multiple linear regression prediction models were developed for CRP, one each for never smokers and ever smokers. An a priori physiologic model was used to guide these analyses, which disallowed the use of other inflammation-sensitive variables such as fibrinogen. In never smokers, the independent predictors were body mass index (+), diabetes status (+), plasmin-antiplasmin complex (+), and the presence of ECG abnormalities (+); this model predicted 15% of the CRP population variance. In ever smokers, the predictors were body mass index (+), plasmin-antiplasmin complex (+), pack-years of smoking (+), HDL cholesterol (-), and ankle-arm blood pressure index (-); this model predicted 42% of the population variance. We conclude that levels of CRP in the healthy elderly are tightly regulated and reflect lifetime exposure to smoking as well as level of obesity, ongoing level of fibrinolysis, diabetes status, and level of subclinical atherothrombotic disease. Moreover, exposure to smoking affects the relation of CRP to these other factors.

Aged↗

PubMed2004

Meta-analysis of age-prevalence patterns in lymphatic filariasis: no decline in microfilaraemia prevalence in older age groups as predicted by models with acquired immunity.

The role of acquired immunity in lymphatic filariasis is uncertain. Assuming that immunity against new infections develops gradually with accumulated experience of infection, models predict a decline in prevalence after teenage or early adulthood. A strong indication for acquired immunity was found in longitudinal data from Pondicherry, India, where Mf prevalence was highest around the age of 20 and declined thereafter. We reviewed published studies from India and Subsaharan Africa to investigate whether their age-prevalence patterns support the models with acquired immunity. By comparing prevalence levels in 2 adult age groups we tested whether prevalence declined at older age. For India, comparison of age groups 20-39 and 40+ revealed a significant decline in only 6 out of 53 sites, whereas a significant increase occurred more often (10 sites). Comparison of older age groups provided no indication that a decline would start at a later age. Results from Africa were even more striking, with many more significant increases than declines, irrespective of the age groups compared. The occurrence of a decline was not related to the overall Mf prevalence and seems to be a chance finding. We conclude that there is no evidence of a general age-prevalence pattern that would correspond to the acquired immunity models. The Pondicherry study is an exceptional situation that may have guided us in the wrong direction.

Adolescent↗

PubMedDate not supplied

A biopsychosocial model to predict caries in preschool children.

The purpose of the study is to assess a multidisciplinary caries-prediction model. Enrolled in the study were 184 low-income children ages 3-5 years old in two Head Start programs in Connecticut. Children were examined by a dentist at baseline and at 1 year for dental caries. Each child also provided a saliva sample to obtain a measure of S. mutans. The children's caregivers completed an interview that assessed oral health behaviors, and cognitive and socioeconomic factors. The prevalence of decay (1 dmfs) increased from 40 to 58% and the number of dmfs increased significantly from 2.5 (7.1) dmfs to 4.5 (8.8) dmfs (P < 0.001) in 1 year. S. mutans did not change significantly. Discriminant function analysis predicting change in caries in the second year from data obtained in the first year showed that S. mutans, dmfs, and toothbrushing significantly predicted caries risk (canonical correlation = 0.5571; x2 = 51; df = 3; P < 0.001). Children with higher dmfs, higher S. mutans, and whose parents reported more frequent brushing had more decay in the second year. None of the other behavioral, cognitive, or demographic factors was significant. The results emphasize the importance of early intervention in preventing dental caries in an underserved population.

Anti-Bacterial Agents↗

PubMed2006

Health services usage by Alaskan injection drug users.

OBJECTIVES: The purpose of this study was to explore health services usage among injection drug users in Anchorage, Alaska. DESIGN: 645 participants were recruited as part of a federally funded study of needle exchanges. They completed a health services usage questionnaire that elicited information on use of a health care provider (physician or nurse) and emergency room services. Chi-square and t-tests were used for the bivariate analyses, and multiple logistic regression was used to develop the final predictive models. RESULTS: The majority of respondents (n = 646) were male (77 percent). Race/ethnicity included 58 percent White, 22 percent Alaska Native, and 20 percent African American. The multivariate model predicting accessing a health care provider (HCP) included ever having had chlamydia (OR 2.7, CI 1.6, 4.5), current income from welfare or public assistance (OR 2.6, CI 1.7, 3.9), current income from disability (OR 5.0, CI 2.2, 11.4), current income from SSI (OR .30, CI .12, .77) and the number of days in the last 30 used opiates other than heroin (OR 1.04, CI 1.002, 1.078). The multivariate model predicting use of an emergency room (ER) was similar to that predicting use of an HCP, with the additional finding of a negative association between being African American and ER use. CONCLUSION: The role of public assistance benefits enabling access to health care for IDUs has policy implications. Large public programs, such as the Indian Health Service, paid for much of the health care received by the IDUs recruited as part of this study.

Adult↗

PubMedDate not supplied

A predictive reinforcement model of dopamine neurons for learning approach behavior.

A neural network model of how dopamine and prefrontal cortex activity guides short- and long-term information processing within the cortico-striatal circuits during reward-related learning of approach behavior is proposed. The model predicts two types of reward-related neuronal responses generated during learning: (1) cell activity signaling errors in the prediction of the expected time of reward delivery and (2) neural activations coding for errors in the prediction of the amount and type of reward or stimulus expectancies. The former type of signal is consistent with the responses of dopaminergic neurons, while the latter signal is consistent with reward expectancy responses reported in the prefrontal cortex. It is shown that a neural network architecture that satisfies the design principles of the adaptive resonance theory of Carpenter and Grossberg (1987) can account for the dopamine responses to novelty, generalization, and discrimination of appetitive and aversive stimuli. These hypotheses are scrutinized via simulations of the model in relation to the delivery of free food outside a task, the timed contingent delivery of appetitive and aversive stimuli, and an asymmetric, instructed delay response task.

Animals↗

PubMed1998

Competitive Effects of Nondisplaceable Organic Compounds on Trichloroethylene Uptake by Activated Carbon. I. Thermodynamic Predictions and Model Sensitivity Analyses.

Theoretical analyses were performed to investigate potential mechanisms affecting observed reductions in uptake of trichloroethylene from the aqueous phase by activated carbon loaded with nondisplaceable organic molecules. Isotherm sensitivity analysis and thermodynamically based competitive adsorption model predictions give a clear and consistent mechanistic interpretation. At low loadings of nondisplaceable organics, the most significant effect is to reduce the number of high-energy sites available to subsequently adsorbed TCE. The loss of high-energy sites causes a significant reduction in site-energy heterogeneity and reduces TCE uptake in low-equilibrium concentration regions (parts per billion) of the isotherm. As the loading of nondisplaceable compounds increases, further reductions in TCE uptake occur; however, further changes in the site-energy heterogeneity are distributed across a wide spectrum of site energies. This suggests a lowering of the average site energy, a reduction in the total number of sites, or both. In terms of TCE isotherms, this corresponds to a roughly constant percentage reduction in uptake over a wide range of equilibrium concentrations, displacing the isotherm downward relative to the uptake axis. Copyright 1998 Academic Press.

Journal Article↗