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PubMedDate not supplied

Evaluation of the predictive value of Fanger's PMV index study in a population of school children. Predicted mean vote.

Fanger's PMV index has been widely used in the last ten years even if it has not yet been sufficiently tested in children. Furthermore recent studies have expressed doubts on the utilization of the index in real situations. We therefore studied the applicability of the PMV index via a survey that involved school children from 8 to 11 years old. The study was conducted comparing the individual objective quantification of thermal comfort (values of PMV index) and the feeling of subjective thermal comfort of people involved (by answering a standardized questionnaire). The PMV index values within the interval +/- 0.5 were grouped in 2 degrees C operative temperature classes; the percentages of subjects who expressed thermal comfort were calculated within these groups. Considering Fanger's assumption (as foreseen by ISO 7730), the percentage of subjects in a condition of thermal discomfort could not be higher than 10% in each group. On the contrary a percentage of dissatisfied persons was obtained that was significantly higher (p < 0.01) than 10% over all the temperature values considered. The number of discrepancies, analyzed even with respect to operative temperature values, was so high that it could not be attributed to the thermal variation that can be measured in the classrooms. Variations due to differences in the thermal resistance of clothing can be excluded since this was taken into consideration individually during the elaboration of the index.

Body Temperature Regulation↗
PubMed1997

Prediction of poor outcome of intensive care unit patients admitted from the emergency department.

OBJECTIVE: To assess whether physicians can identify very low likelihood of survival and very low likelihood of favorable functional outcome in adult nontrauma patients before admission to the intensive care unit (ICU) from the emergency department (ED). DESIGN: Prospective survey. SETTING: University hospital ED and ICU. PARTICIPANTS AND PATIENTS: Critical care fellows and ED physicians and all adult nontrauma patients admitted to the ICU from the ED over 1 yr. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The survey compared predictions of poor outcome from three sources: critical care fellows, ED physicians, and the admission Mortality Probability Model (MPM0). All patients were followed until hospital death or hospital discharge. Six-month follow-up data were obtained for patients predicted to have a < 2% chance of surviving with favorable functional outcome. In the ED, critical care fellows and ED physicians predicted likelihood of patient survival and likelihood of favorable functional outcome. MPM0 estimates of mortality were determined. The sensitivities, specificities, and positive predictive values were calculated for the predictions of < 2% survival and the predictions of < 2% chance of favorable functional outcome made by each prediction group. Complete data were obtained on 236 (96%) of 243 eligible patients. With regard to hospital mortality rate, fellows' predictions had a sensitivity of 27%, a specificity of 99%, and a positive predictive value of 88%; ED physicians' predictions had a sensitivity of 24%, a specificity of 98%, and a positive predictive value of 81%; and MPM0 predictions had a sensitivity of 2%, a specificity of 100%, and a positive predictive value of 100%. With regard to mortality rate combined with poor functional outcome, fellows' predictions had a sensitivity of 35%, a specificity of 99%, and a positive predictive value of 96%; ED physicians' predictions had a sensitivity of 37%, a specificity of 99%, and a positive predictive value of 96%. CONCLUSIONS: If a cutoff point of < 2% predicted survival is used in the triage of patients away from the ICU, the MPM0 has too low a sensitivity to be used as an effective screen. The low sensitivities and relatively low positive predictive values with wide confidence intervals of physician predictions of < 2% survival also preclude their use in triage. The addition of functional outcome as an end point improves the sensitivity, specificity, and positive predictive value of subjective predictions, making triage of patients away from the ICU at the time of ED evaluation a realistic possibility.

Adult↗
PubMed2004

Predicting respiratory motion for four-dimensional radiotherapy.

Adapting radiation delivery to respiratory motion is made possible through corrective action based on real-time feedback of target position during respiration. The advantage of this approach lies with its ability to allow tighter margins around the target while simultaneously following its motion. A significant hurdle to the successful implementation of real-time target-tracking-based radiation delivery is the existence of a finite time delay between the acquisition of target position and the mechanical response of the system to the change in position. Target motion during the time delay leads to a resultant lag in the system's response to a change in tumor position. Predicting target position in advance is one approach to ensure accurate delivery. The aim of this manuscript is to estimate the predictive ability of sinusoidal and adaptive filter-based prediction algorithms on multiple sessions of patient respiratory patterns. Respiratory motion information was obtained from recordings of diaphragm motion for five patients over 60 sessions. A prediction algorithm that employed both prediction models-the sinusoidal model and the adaptive filter model-was developed to estimate prediction accuracy over all the sessions. For each session, prediction error was computed for several time instants (response time) in the future (0-1.8 seconds at 0.2-second intervals), based on position data collected over several signal-history lengths (1-7 seconds at 1-second intervals). Based on patient data included in this study, the following observations are made. Qualitative comparison of predicted and actual position indicated a progressive increase in prediction error with an increase in response time. A signal-history length of 5 seconds was found to be the optimal signal history length for prediction using the sinusoidal model for all breathing training modalities. In terms of overall error in predicting respiratory motion, the adaptive filter model performed better than the sinusoidal model. With the adaptive filter, average prediction errors of less than 0.2 cm (1sigma) are possible for response times less than 0.4 seconds. In comparing prediction error with system latency error (no prediction), the adaptive filter model exhibited lesser prediction errors as compared to the sinusoidal model, especially for longer response time values (>0.4 seconds). At smaller response time values (<0.4 seconds), improvements in prediction error reduction are required for both predictive models in order to maximize gains in position accuracy due to prediction. Respiratory motion patterns are inherently complex in nature. While linear prediction-based prediction models perform satisfactorily for shorter response times, their prediction accuracy significantly deteriorates for longer response times. Successful implementation of real-time target-tracking-based radiotherapy requires response times less than 0.4 seconds or improved prediction algorithms.

Algorithms↗
PubMed2003

Predictions of recall and study strategy decisions after diffuse brain injury.

OBJECTIVE: To investigate relationships between self-monitoring operationalized by predicting recall and study strategy decisions made by adults with diffuse, acquired brain injury (ABI) and adults without ABI. RESEARCH DESIGN AND METHODS: Eighteen adults with ABI and 16 without ABI studied two lists of unrelated noun-pairs, made item-by-item immediate and delayed recall predictions and selected items (after predictions) to restudy again. The computer selected items for restudy based on the lowest prediction rating (i.e. unlikely to recall). A mixed list design was used to balance item selection (self or computer,within-lists) by type of prediction (immediate or delayed, between-lists). Recall was tested prior to and after restudying. HYPOTHESES: Delayed recall predictions would be more accurate than immediate recall predictions; participants would select items for restudy that corresponded with 'lower' delayed predictions (i.e. less likely to recall) and 'higher' immediate predictions (i.e. more likely to recall); for adults with ABI, recall would improve the most from restudying items selected after delayed predictions; and that recall predictive accuracy and study selection decisions are independent processes. RESULTS: Delayed recall predictions were more accurate than immediate recall predictions, though adults with ABI tended to be less accurate than controls. Both groups selected items for restudy that had relatively low prediction ratings irrespective of prediction timing. Of adults with ABI, those with low recall prior to restudy selected items that had 'high' immediate predictions (i.e. likely to recall). However, there was no greater benefit to recall using this strategy. For adults with ABI, recall improved the most from restudying items that were self-selected after delayed predictions, whereas controls' recall improved, irrespective of prediction and selection timing. Between-person correlations revealed no relationship between recall predictive accuracy and study selection strategies. CONCLUSIONS: These findings imply that adults with ABI should base strategy decisions on delayed predictions rather than on 'in the moment' immediate ones, selecting items that they have predicted will be difficult to recall. Continued efforts to disambiguate self-monitoring from strategy decisions are required before direct clinical applications can be made.

Adult↗