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PubMed2005

Risk stratification models fail to predict hospital costs of cardiac surgery patients.

BACKGROUND: The aim of this prospective study was to determine if commonly used risk stratification models can predict total hospital costs in cardiac surgical patients. METHODS: Between October 1st and December 31st 2003, all consecutive adult patients undergoing cardiac surgery on CPB at our institution were classified using seven risk stratification scoring systems: EuroSCORE, Cleveland, Parsonnet, Ontario, French, Pons, and CABDEAL. Total hospital costs for each patient were calculated on a daily basis including preoperative diagnostic tests, operating room costs, disposable materials, drugs, blood components, costs for personnel, and hospital fixed-costs. Linear regression analysis was used to determine the correlation between costs and the seven risk stratifications models as well as length of stay (LOS) on ICU. The Spearman correlation coefficient was calculated from the regression line, and an analysis of residuals was performed to determine the quality of the regression. RESULTS: A total of 252 patients were operated for CABG (n=175), valve (n=39), CABG plus valve (n=21), thoracic aorta (n=13) and miscellaneous (2 myxoma, 1 ASD, 1 pulmonary embolism). Mean age of the patients was 66.0+/-11.4 years, 29.4% were female. LOS on ICU was 3.3+/-6.3 days and the 30-day mortality rate was 6.7%. Spearman correlation between the seven risk stratification models and hospital costs was below r=0.32 (p=0.0001), but was r=0.94 (p=0.0001) between ICU LOS and costs. CONCLUSIONS: Total hospital costs can be identified by length of ICU stay. None of the common risk stratification models accurately predicted total hospital costs in cardiac surgical patients.

Aged↗

PubMedDate not supplied

A new model for risk stratification and delivery of cardiovascular rehabilitation services in the long-term clinical management of patients with coronary artery disease.

This model for risk stratification includes variables that classify patients for Risk of Event similar to current models of risk stratification, as well as variables that stratify patients for Risk of Progression of Atherosclerosis by established risk factors. Categories of risk are established using accepted data from the literature for each risk factor that targets regression or plaque stabilization as the goal for Low Risk. A case-rate charging system and the proposed removal of time restrictions for length of cardiovascular rehabilitation fit neatly into the present climate for health care. Health maintenance organizations will be seeking programs that use similar models to address cost issues inherent in cardiovascular rehabilitation programs under current fee-for-service models. Improved outcomes will also be targets for these programs and case-management lends itself to disease management, thus, improved outcomes. Tracking outcomes becomes even more important to both the provider and the insurer because results drive referrals. Likewise, removal of the time restriction for cardiovascular rehabilitation allows programs to individualize care and to target risk factors that are not only most deleterious, but also where patients show readiness for change. The changing environment of health care virtually mandates change in cardiovascular rehabilitation. It is imperative that programs manage the disease process, are effective in achieving outcomes that affect both patient function and the disease process, and are cost effective. This model for risk stratification and delivery of services addresses these requirements and provides a beginning for implementing these changes in cardiovascular rehabilitation.

Coronary Artery Disease↗

PubMed1990

Which types of perinatal events are predictable? A look at a risk score model.

This study describes the association of a risk factor model for complicated delivery, perinatal morbidity and perinatal mortality with each of various types of delivery complications, types of perinatal morbidity and causes of perinatal mortality. The material comprises a total cohort, 4,066 pregnant women with singletons in a Danish county, and their newborn infants, of whom 494 (12%) had clinical morbidity during the first 5 days of life; 28 (0.7%) died perinatally. A set of 20 risk factors, identifiable before pregnancy, at any time during the pregnancy or at term, was devised by joining existing models for prediction of complicated delivery and of perinatal morbidity and mortality. Metabolic and disproportion-related events were well predicted by the model, inertia-related ones less so, and placental conditions not at all, except for abruption. All types of neonatal morbidity (except sepsis) were well predicted, as were deaths. The strongest predictors of perinatal death were signs of hydramnios (RR = 16.1) and growth retardation (RR = 7.2). The 20 risk factors affected 43% of the population, predicting 57% of the unfavorable perinatal events.

Cohort Studies↗

PubMed2004

The differences between claim-based health risk adjustment models and cost prediction models.

There has been a significant increase in interest in using risk assessment tools with administrative claims data for provider profiling, provider payment, underwriting and disease/case management. The tools can be classified into two types: risk adjustment models and cost prediction models. The differences between the two models have not been well recognized. This paper explains the differences in terms of the objectives, the applications, and the accuracy of evaluations.

Insurance Claim Review↗

PubMed1995

Prospective study of hospitalization for asthma. A preliminary risk factor model.

We conducted an exploratory analysis of several prospectively obtained objective measures of disease activity to derive a predictive model of hospitalization for asthma among 310 adults, ages 18 to 50 yr, with moderate to severe asthma. Baseline characteristics associated with increased risk of hospitalization in the succeeding year include (1) prior year hospitalization, (2) moderate or severe respiratory impairment, (3) a medication regimen consistent with severe asthma, (4) a history of significant systemic steroid use, (5) maximum overnight PEF variability > 40%, and (6) mean evening PEF < 60% of predicted (relative risk = 6.5, 6.9, 8.1, 3.7, 3.0, and 3.2, respectively). Recursive partitioning analysis, depicted as a "classification tree," provided a more sensitive (94%) and specific (68%) multivariate description of the data set than either logistic regression (87 and 48%, respectively) or a simple additive risk model (46 and 93%, respectively). Patients with very high (> 50%), moderately elevated (10 to 15%), and very low (< 5%) risk of hospitalization were identified on the basis of particular combinations of prior hospitalization history, level of respiratory impairment, and medication regimen. Overnight variability and mean evening PEF measured at home over a 2-wk period proved less informative for risk stratification than respiratory impairment determined once at baseline by office spirometry. The findings warrant replication and extension in other populations with the goal of developing decision rules for risk stratification and effective interventions for risk reduction.

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↗

PubMed2005

Risk assessment model for venothromboembolism in post-hospitalized patients.

AIM: Venothromboembolism (VTE) is an important condition in hospitalized patients accounting for significant morbidity and mortality, and the risk of VTE often continues post-hospitalization. Although risk assessment models have been developed to predict the risk of deep venous thrombosis (DVT) in hospitalized patients, no models have been developed that determine the risk of DVT during the post-hospitalization period. The objective of this study was to create a risk profile using risk factor assessment that could be used to predict which patients are at highest risk of developing DVT within 60 days following hospital discharge. METHODS: The computerized medical records of 380 patients (190 with DVT and 190 without DVT) who received care from 1995-2002 and were subsequently re-hospitalized within 60 days of discharge were retrospectively reviewed. Univariate and multivariate logistic regression analyses were conducted to identify risk variables related to VTE. A novel risk assessment model was created using risk factors from the logistic regression analyses. RESULTS: The prevalence of VTE was found to be 93.2% (69/74) in the high-risk category, 52.9% (109/206) in the moderate-risk category, and 12% (12/100) in the low-risk category. CONCLUSIONS: Once validated, this risk assessment model may be applied to identify patients who may be at increased risk of developing VTE post-hospitalization. Those at high risk should be considered for anticoagulation therapy during the post-hospitalization period. Availability of a risk profile using risk factor assessment to guide decisions related to anticoagulation therapy will have important ramifications relative to patient outcomes including morbidity, mortality, and reductions in VTE-associated cost.

Hospitalization↗

PubMedDate not supplied

The value of a baseline caries risk assessment model in the primary dentition for the prediction of caries incidence in the permanent dentition.

To establish a reliable screening method for caries prediction and to identify predominant risk factors, this study tested whether a cross-sectional caries risk model assessed at age 7 could be used to predict future caries onset in the permanent first molars at age 10 in 3,303 children born in 1989. As prediction variables, assessing the believed risk, baseline data at age 7 on oral health status, oral hygiene level, oral health behaviour and sociodemographic factors were used. The real risk, based on data collected for the first permanent molars during the follow-up, was assessed by different approaches. Cumulative incidence during the 3-year observation period was 31.6%, ranging from 22.4% in the believed low-risk group to 43.2% in the believed high-risk group. A stepwise logistic regression analysis was performed with net caries increment as outcome measure, adjusted for the real time at risk, using eruption times. Baseline dmfs and occlusal and buccal plaque indices were highly significant for having a high caries increment in permanent first molars with respective odds ratios of 1.07, 1.43 and 1.35. Brushing less than once a day and the daily use of sugar-containing drinks between meals were confirmed as risk factors (OR 2.43 and 1.25, respectively). The logistic regression analysis provided a sensitivity of 59-66% and a specificity of 65.7-72.8%, which indicates that the risk marker did not have an important predictive power. None of the socio-demographic and behavioural variables had enough predictive power at community level to be useful for identifying caries susceptible children. Even the power of dmfs at baseline must be considered modest.

Beverages↗

PubMed2001

Prediction of operative mortality after valve replacement surgery.

OBJECTIVES: We sought to develop national benchmarks for valve replacement surgery by developing statistical risk models of operative mortality. BACKGROUND: National risk models for coronary artery bypass graft surgery (CABG) have gained widespread acceptance, but there are no similar models for valve replacement surgery. METHODS: The Society of Thoracic Surgeons National Cardiac Surgery Database was used to identify risk factors associated with valve surgery from 1994 through 1997. The population was drawn from 49,073 patients undergoing isolated aortic valve replacement (AVR) or mitral valve replacement (MVR) and from 43,463 patients undergoing CABG combined with AVR or MVR. Two multivariable risk models were developed: one for isolated AVR or MVR and one for CABG plus AVR or CABG plus MVR. RESULTS: Operative mortality rates for AVR, MVR, combined CABG/AVR and combined CABG/ MVR were 4.00%, 6.04%, 6.80% and 13.29%, respectively. The strongest independent risk factors were emergency/salvage procedures, recent infarction, reoperations and renal failure. The c-indexes were 0.77 and 0.74 for the isolated valve replacement and combined CABG/valve replacement models, respectively. These models retained their predictive accuracy when applied to a prospective patient population undergoing operation from 1998 to 1999. The Hosmer-Lemeshow goodness-of-fit statistic was 10.6 (p = 0.225) for the isolated valve replacement model and 12.2 (p = 0.141) for the CABG/valve replacement model. CONCLUSIONS: Statistical models have been developed to accurately predict operative mortality after valve replacement surgery. These models can be used to enhance quality by providing a national benchmark for valve replacement surgery.

Aged↗

PubMed1998

Cardiac surgical mortality: comparison among different additive risk-scoring models in a multicenter sample.

OBJECTIVE: To compare the performance of several risk-scoring models to predict surgical mortality following open heart surgery. DESIGN: A prospective observational study. SETTING: Seven tertiary cardiac centers (3 private and 4 public and teaching hospitals) in Catalonia (Spain). PATIENTS: A consecutive sample of 1287 patients submitted to open heart surgery during a 6 1/2-month period (February 14, 1994, to August 31, 1994). INTERVENTION: None. MAIN OUTCOME MEASURE: Model discrimination capability was assessed with the c-statistic. A chi(2) test to compare observed and predicted mortality rates was used as a measure of model calibration. Performance of centers was evaluated through the standardized mortality ratio and using the center as an indicator variable in a logistic regression model. The agreement among models for individual predictions was tested using weighted K statistics. RESULTS: Models developed in other health care contexts showed, as expected, lower c-statistics and an inappropriate calibration. There were no statistically significant differences among hospitals after adjusting for baseline patients' risk factors with the use of any of the different models. Models also agree in the standardized rank of centers. Weighted K statistics indicated poor agreement among models for individual patient risk prediction. CONCLUSIONS: Models can be a useful tool to compare providers' performance and to give a more in-depth look at the process of care when appropriately customized to the context. Severity-adjusted models can also play a role in supporting the informed and subjective surgeon's assessment, but it is inappropriate to use them for individual predictions.

Cardiac Surgical Procedures↗

PubMed1996

Developments in microbiological risk assessment models for drinking water--a short review.

Microbiological risk assessment (MRA) is emerging method to predict the risks of infection from waterborne pathogens (e.g. rotavirus and Cryptosporidium parvum) in the drinking water supply. The objectives of this paper are to review the appropriateness of current models, with emphasis on pathogen exposures through drinking water, and to consider the information necessary to further their development. Calculating pathogen exposures in MRA is currently limited by the fact that pathogen density data for drinking water supplies are only available for very large volume samples--much larger than imbibed daily by any consumer. To develop MRA, information is needed on how pathogens are dispersed within those volumes at the resolution of volumes typically consumed daily by individuals. Available evidence suggests that micro-organisms, including pathogens, are clustered to some degree, even within small volumes, exposing some drinking water consumers to much higher doses than others. By assuming pathogens are randomly dispersed, current models overestimate the risk from the more infectious agents (e.g. rotaviruses) but underestimate the risk from less infectious pathogens (e.g. C. parvum). Approaches to modelling pathogen densities in drinking water from source water data and treatment removal efficiencies require additional information on the degree to which treatment processes (e.g. filtration and coagulation) increase pathogen clustering. The missing information could be obtained from large-scale pilot plant studies.

Cryptosporidiosis↗

PubMed2006

Uncertainty analysis of relative biological effectiveness of alpha-radiation for human lung exposure.

Assessment of relative biological effectiveness (RBE) for a radiation in the cases of inhalation of radon progeny and incorporation of plutonium in lung is based on simulation of lung cancer radiation risk for alpha and external reference types of radiation. Specific radiation risk models developed on the results of direct epidemiological studies are used for simulation. These include published risk models for nuclear workers of the Mayak facilities in the former Soviet Union exposed to incorporated plutonium (Kreisheimer et al., 2003; Gilbert et al., 2004) and underground miners exposed to radon progenies (BEIR VI, 1999). Additionally, a lung cancer risk model is developed for a case of population indoor radon exposure. Lung cancer risk related to external exposure is estimated using the risk model developed for the analyses of Japanese atomic bomb survivors (Preston et al., 2003). Uncertainties of risk models parameters are considered and the uncertainties of RBE are estimated using the results of lifetime lung cancer risk simulation, which is done implementing a Monte Carlo approach. Estimated median value of RBE in case of indoor radon exposure is 1.5 with 90% range 0.4-7. In the case of the two models developed by BEIR VI for lung cancer risk due to radon exposure in underground miners, the median values of RBE are 2.1 and 4.4 with 90% ranges 0.3-17 and 0.7-45, respectively. The two different models for lung cancer risk related to plutonium exposure resulted in close estimates of RBE: median value of 12 and 13 with 90% range 4-104 and 4-136, respectively.

Adult↗

PubMed2026

Predictive Models for Hypoglycemia Risk in Haemodialysis Patients With Diabetic Kidney Disease: Systematic Review and Meta-Analysis.

AIM: To provide evidence for selecting and developing reliable clinical assessment tools for hypoglycemia in diabetic kidney disease patients during haemodialysis. DESIGN: Review. METHODS: Systematic searches were performed in 9 Chinese and English databases to collect literature regarding the development of hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease. Two reviewers independently performed literature screening, data extraction, risk-of-bias assessment, and applicability evaluation. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias and applicability of the included studies. Meta-analysis was conducted using R software. DATA SOURCES: CNKI, Wanfang, VIP, CBM, PubMed, Cochrane Library, EMbase, Web of Science, and CINAHL. The search period covered from the establishment date of each database to December 2025. RESULTS: Six studies, comprising six prediction models, were included. Two studies performed internal validation, and three conducted external validation. All models reported the area under the curve, ranging from 0.813 to 0.866, and calibration measures. Four studies were rated as having a high risk of bias, while all six demonstrated good overall applicability. The meta-analysis showed that the pooled AUC value of the six studies was 0.846 (95% CI: 0.823-0.867). CONCLUSION: Research on hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease remains in the developmental stage. Although the included prediction models exhibited satisfactory apparent discriminatory ability and clinical applicability, most of the original studies suffered from a high risk of bias and lacked adequate validation. The true predictive performance and clinical application value of these models remain to be further verified. Accordingly, routine and unconditional clinical application is not recommended at this stage. Future studies should include more high-quality, multicenter external validation and develop models with high generalizability, favourable clinical applicability, and robust predictive performance to facilitate early identification of hypoglycemia risk in this population. IMPACT: This study systematically evaluated the hypoglycemia risk prediction models for diabetic kidney disease patients during haemodialysis, and the research on hypoglycemia risk prediction models for maintenance haemodialysis patients during dialysis is still in the development stage. This study provides a reference for clinical medical staff to select or develop hypoglycemia risk prediction and assessment tools for diabetic kidney disease patients during haemodialysis. REPORTING METHOD: This study was conducted in accordance with the relevant guidelines of the EQUATOR Network and followed the TRIPOD-SRMA Checklist. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. TRIAL REGISTRATION: PROSPERO: CRD420251243352.

Humans↗

PubMed2006

Risk assessment: A model for predicting cross-species transmission of simian foamy virus from macaques (M. fascicularis) to humans at a monkey temple in Bali, Indonesia.

Contact between humans and nonhuman primates (NHPs) frequently occurs at monkey temples (religious sites that have become associated with free-ranging populations of NHPs) in Asia, creating the potential for NHP-human disease transmission. In March 2003 a multidisciplinary panel of experts participated in a workshop designed to model the risk of NHP-human pathogen transmission. The panel developed a risk assessment model to describe the likelihood of cross-species transmission of simian foamy virus (SFV) from temple macaques (Macaca fascicularis) to visitors at monkey temples. SFV is an enzootic simian retrovirus that has been shown to be transmitted from NHPs to humans. In operationalizing the model field data, laboratory data and expert opinions were used to estimate the likelihood of SFV transmission within this context. This model sets the stage for a discussion about modeling as a risk assessment tool and the kinds of data that are required to accurately predict transmission.

Animals↗

PubMed2002

Evaluating a risk-based model for mammographic screening of women in their forties.

BACKGROUND: A risk-based model was developed for recommendations for mammography screening of women in their 40s. We determined retrospectively the proportion of women already diagnosed in their 40s with breast cancer (BC) who would have been recommended for mammography screening by this model. METHODS: Information was obtained from 404 women who were diagnosed with BC in their 40s from 1990 to 1998 at a large midwestern hospital. Data on BC detection method, mammography history, and BC risk factors existing before diagnosis were obtained from 353 of these women by mailed questionnaires. Data for an additional 51 women who had died were obtained by chart review. Two algorithms, the Exact Age Procedure (EAP) and the Grouped Age Procedure (GAP), were used to calculate the proportion of women who would have been recommended for mammography screening based on age, race, and BC risk factors. RESULTS: Of women diagnosed with BC in their 40s, 74.5% would have been recommended for screening by the EAP and 70.5% by the GAP. The model would have recommended screening for approximately one half of the women who had no conventional risk factors (54% by EAP, 48% by GAP). CONCLUSION: Although use of the risk-based model could result in more recommendations for screening than individual recommendations based on patient-physician discussions about the woman's risk factors, one in four women diagnosed with breast cancer in their 40s would not have been recommended for mammography screening.

Adult↗

PubMed2006

A cumulative risk factor model for early identification of academic difficulties in premature and low birth weight infants.

OBJECTIVES: Premature and low birth weight children have a high prevalence of academic difficulties. This study examines a model comprised of cumulative risk factors that allows early identification of these difficulties. METHODS: This is a secondary analysis of data from a large cohort of premature (<37 weeks gestation) and LBW (<2500 g) children. The study subjects were 8 years of age and 494 had data available for reading achievement and 469 for mathematics. Potential predictor variables were categorized into 4 domains: sociodemographic, neonatal, maternal mental health and early childhood (ages 3 and 5). Regression analysis was used to create a model to predict reading and mathematics scores. RESULTS: Variables from all domains were significant in the model, predicting low achievement scores in reading (R (2) of 0.49, model p-value < .0001) and mathematics (R (2) of 0.44, model p-value < .0001). Significant risk factors for lower reading scores, were: lower maternal education and income, and Black or Hispanic race (sociodemographic); lower birth weight and male gender (neonatal); lower maternal responsivity (maternal mental health); lower intelligence, visual-motor skill and higher behavioral disturbance scores (early childhood). Lower mathematics scores were predicted by lower maternal education, income and age and Black or Hispanic race (sociodemographic); lower birth weight and higher head circumference (neonatal); lower maternal responsivity (maternal mental health); lower intelligence, visual-motor skill and higher behavioral disturbance scores (early childhood). CONCLUSIONS: Sequential early childhood risk factors in premature and LBW children lead to a cumulative risk for academic difficulties and can be used for early identification.

Child↗

PubMed2004

Use of advances in technology for maritime risk assessment.

The maritime industry is moving toward a "goal-setting" risk-based regime. This opens the way to safety engineers to explore and exploit flexible and advanced risk modeling and decision-making approaches in the design and operation processes. In this article, following a brief review of the current status of maritime risk assessment, a design/operation selection framework and a design/operation optimization framework are outlined. A general discussion of control engineering techniques and their application to risk modeling and decision making is given. Four novel risk modeling and decision-making approaches are then outlined with illustrative examples to demonstrate their use. Such approaches may be used as alternatives to facilitate risk modeling and decision making in situations where conventional techniques cannot be appropriately applied. Finally, recommendations on further exploitation of advances in general engineering and technology are suggested with respect to risk modeling and decision making.

Algorithms↗

PubMed1996

Evaluation of the white finger risk prediction model in ISO 5349 suggests need for prospective studies.

The risk prediction model for white fingers in Annex A of ISO 5349 is not likely to offer protection from all tools and all work processes. It is also probable that some work place changes it has initiated are either redundant or lack the intended effect. The main reasons for these shortcomings are the following. The often demonstrated disagreement between predicted and observed white fingers occurrence may be related to the fact that the model is based on latency data. This leads to an overestimation, to an unknown extent, of true group risks. A possible healthy worker effect, resulting in underestimation, has not been considered, and uncertainty because of recall bias is connected with using latency as effect variable in a slowly developing disorder like white fingers. The diagnostic criteria for white fingers have varied over the years, causing a possible inclusion of circulatory disturbances other than those induced by vibration. Among insufficiently clarified matters unrelated to vibration are variations in individual susceptibility and other host factors that modify vibration effects, uncertainty concerning daily or total effective exposure, and the fact that variation in work methods and processes as well as ergonomic factors other than vibration tend to make different groups incomparable form the viewpoint of risk of injury. Lack of sufficient data on vibration measurements and employment durations add to the uncertainty, as do variations in tool conditions (grinder wheels, etc) and inherent difficulties in measurement. Finally, the ISO 5349 frequency-weighting curve only relates to acute sensory effects rather than chronic effects on vascular functions like white fingers, and directional difference in sensitivity has not been incorporated in the curve. Data on exposure-response relationships are needed from prospective studies that monitor the dose of exposure to special vibration types and all relevant environmental agents, employ diagnostics with good sensitivity, specificity and predictive value, and pay attention to environmental or individual confounding factors and effect modifiers. Before such data are available, the ISO 5349 model should not be used for risk prediction. It can serve, however, as an incentive for manufacturers to produce tools that vibrate less, and for employers to implement practical measures in order to reduce the total and dose of effective exposure.

Fingers↗