Meta- analysis demonstrated fallers perform significantly worse than non-fallers on all measures analysed except the Timed Up and Go Cognitive ( p < 0.05 ) , but discriminative ability of the measures is commonly not reported .
Of those reported , the Activities-specific Balance Confidence Scale had the highest area under the receiver operating characteristic curve value ( 0.92 ) , but without reporting corresponding measures of clinical utility .
Conclusion : Clinical measures of balance differ significantly between fallers and non-fallers but have poor predictive ability for falls risk in people with multiple sclerosis
Objective : To determine the ability of clinical measures of balance to distinguish fallers from non-fallers and to determine their predictive validity in identifying those at risk of falls .
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration
In medicine , numerous decisions are made by care providers , often in shared decision making , on the basis of an estimated probability that a specific disease or condition is present ( diagnostic setting ) or a specific event will occur in the future ( prognostic setting ) in an individual . In the diagnostic setting , the probability that a particular disease is present can be used , for example , to inform the referral of patients for further testing , to initiate treatment directly , or to reassure patients that a serious cause for their symptoms is unlikely . In the prognostic context , predictions can be used for planning lifestyle or therapeutic decisions on the basis of the risk for developing a particular outcome or state of health within a specific period ( 13 ) . Such estimates of risk can also be used to risk-stratify participants in therapeutic intervention trials ( 47 ) . In both the diagnostic and prognostic setting , probability estimates are commonly based on combining information from multiple predictors observed or measured from an individual ( 1 , 2 , 810 ) . Information from a single predictor is often insufficient to provide reliable estimates of diagnostic or prognostic probabilities or risks ( 8 , 11 ) . In virtually all medical domains , diagnostic and prognostic multivariable ( risk ) prediction models are being developed , vali date d , up date d , and implemented with the aim to assist doctors and individuals in estimating probabilities and potentially influence their decision making . A multivariable prediction model is a mathematical equation that relates multiple predictors for a particular individual to the probability of or risk for the presence ( diagnosis ) or future occurrence ( prognosis ) of a particular outcome ( 10 , 12 ) . Other names for a prediction model include risk prediction model , predictive model , prognostic ( or prediction ) index or rule , and risk score ( 9 ) . Predictors are also referred to as covariates , risk indicators , prognostic factors , determinants , test results , ormore statisticallyindependent variables . They may range from demographic characteristics ( for example , age and sex ) , medical historytaking , and physical examination results to results from imaging , electrophysiology , blood and urine measurements , pathologic examinations , and disease stages or characteristics , or results from genomics , proteomics , transcriptomics , pharmacogenomics , metabolomics , and other new biological measurement platforms that continuously emerge . Diagnostic and Prognostic Prediction Models Multivariable prediction models fall into 2 broad categories : diagnostic and prognostic prediction models ( Box A ) . In a diagnostic model , multiplethat is , 2 or morepredictors ( often referred to as diagnostic test results ) are combined to estimate the probability that a certain condition or disease is present ( or absent ) at the moment of prediction ( Box B ) . They are developed from and to be used for individuals suspected of having that condition . Box A. Schematic representation of diagnostic and prognostic prediction modeling studies . The nature of the prediction in diagnosis is estimating the probability that a specific outcome or disease is present ( or absent ) within an individual , at this point in timethat is , the moment of prediction ( T= 0 ) . In prognosis , the prediction is about whether an individual will experience a specific event or outcome within a certain time period . In other words , in diagnostic prediction the interest is in principle a cross-sectional relationship , whereas prognostic prediction involves a longitudinal relationship . Nevertheless , in diagnostic modeling studies , for logistical reasons , a time window between predictor ( index test ) measurement and the reference st and ard is often necessary . Ideally , this interval should be as short as possible without starting any treatment within this period . Box B. Similarities and differences between diagnostic and prognostic prediction models . In a prognostic model , multiple predictors are combined to estimate the probability of a particular outcome or event ( for example , mortality , disease recurrence , complication , or therapy response ) occurring in a certain period in the future . This period may range from hours ( for example , predicting postoperative complications [ 13 ] ) to weeks or months ( for example , predicting 30-day mortality after cardiac surgery [ 14 ] ) or years ( for example , predicting the 5-year risk for developing type 2 diabetes [ 15 ] ) . Prognostic models are developed and are to be used in individuals at risk for developing that outcome . They may be models for either ill or healthy individuals . For example , prognostic models include models to predict recurrence , complications , or death in a certain period after being diagnosed with a particular disease . But they may also include models for predicting the occurrence of an outcome in a certain period in individuals without a specific disease : for example , models to predict the risk for developing type 2 diabetes ( 16 ) or cardiovascular events in middle-aged nondiseased individuals ( 17 ) , or the risk for preeclampsia in pregnant women ( 18 ) . We thus use prognostic in the broad sense , referring to the prediction of an outcome in the future in individuals at risk for that outcome , rather than the narrower definition of predicting the course of patients who have a particular disease with or without treatment ( 1 ) . The main difference between a diagnostic and prognostic prediction model is the concept of time . Diagnostic modeling studies are usually cross-sectional , whereas prognostic modeling studies are usually longitudinal . In this document , we refer to both diagnostic and prognostic prediction models as prediction models , highlighting issues that are specific to either type of model . Development , Validation , and Updating of Prediction Models Prediction model studies may address the development of a new prediction model ( 10 ) , a model evaluation ( often referred to as model validation ) with or without updating of the model [ 1921 ] ) , or a combination of these ( Box C and Figure 1 ) . Box C. Types of prediction model studies . Figure 1 . Types of prediction model studies covered by the TRIPOD statement . D = development data ; V = validation data . Model development studies aim to derive a prediction model by selecting predictors and combining them into a multivariable model . Logistic regression is commonly used for cross-sectional ( diagnostic ) and short-term ( for example 30-day mortality ) prognostic outcomes and Cox regression for long-term ( for example , 10-year risk ) prognostic outcomes . Studies may also focus on quantifying the incremental or added predictive value of a specific predictor ( for example , newly discovered ) ( 22 ) to a prediction model . Quantifying the predictive ability of a model on the same data from which the model was developed ( often referred to as apparent performance [ Figure 1 ] ) will tend to give an optimistic estimate of performance , owing to overfitting ( too few outcome events relative to the number of c and i date predictors ) and the use of predictor selection strategies ( 2325 ) . Studies developing new prediction models should therefore always include some form of internal validation to quantify any optimism in the predictive performance ( for example , calibration and discrimination ) of the developed model and adjust the model for overfitting . Internal validation techniques use only the original study sample and include such methods as bootstrapping or cross-validation . Internal validation is a necessary part of model development ( 2 ) . After developing a prediction model , it is strongly recommended to evaluate the performance of the model in other participant data than was used for the model development . External validation ( Box C and Figure 1 ) ( 20 , 26 ) requires that for each individual in the new participant data set , outcome predictions are made using the original model ( that is , the published model or regression formula ) and compared with the observed outcomes . External validation may use participant data collected by the same investigators , typically using the same predictor and outcome definitions and measurements , but sample d from a later period ( temporal or narrow validation ) ; by other investigators in another hospital or country ( though disappointingly rare [ 27 ] ) , sometimes using different definitions and measurements ( geographic or broad validation ) ; in similar participants , but from an intentionally different setting ( for example , a model developed in secondary care and assessed in similar participants , but selected from primary care ) ; or even in other types of participants ( for example , model developed in adults and assessed in children , or developed for predicting fatal events and assessed for predicting nonfatal events ) ( 19 , 20 , 26 , 2830 ) . In case of poor performance ( for example , systematic miscalibration ) , when evaluated in an external validation data set , the model can be up date d or adjusted ( for example , recalibrating or adding a new predictor ) on the basis of the validation data set ( Box C ) ( 2 , 20 , 21 , 31 ) . R and omly splitting a single data set into model development and model validation data sets is frequently done to develop and vali date a prediction model ; this is often , yet erroneously , believed to be a form of external validation . However , this approach is a weak and inefficient form of internal validation , because not all available data are used to develop the model ( 23 , 32 ) . If the available development data set is sufficiently large , splitting by time and developing a model using data from one period and evaluating its performance using the data from the other period ( temporal validation ) is a stronger approach . With a single data set , temporal splitting and model validation can be considered intermediate between internal and external validation . Incomplete and Inaccurate Reporting Prediction models are becoming increasingly abundant in the medical literature ( 9 , 33 , 34 ) , and policymakers are
Meta- analysis demonstrated fallers perform significantly worse than non-fallers on all measures analysed except the Timed Up and Go Cognitive ( p < 0.05 ) , but discriminative ability of the measures is commonly not reported .
Of those reported , the Activities-specific Balance Confidence Scale had the highest area under the receiver operating characteristic curve value ( 0.92 ) , but without reporting corresponding measures of clinical utility .
Conclusion : Clinical measures of balance differ significantly between fallers and non-fallers but have poor predictive ability for falls risk in people with multiple sclerosis
Objective : To determine the ability of clinical measures of balance to distinguish fallers from non-fallers and to determine their predictive validity in identifying those at risk of falls .