La creciente importancia del rendimiento físico en la valoración geriátrica integral / The increasing relevance of physical performance in global geriatric assessment

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Test Alusti: nueva escala de valoración del rendimiento físico para la población geriátrica / Alusti test: new scale for assessment of physical performance in the geriatric population

Introducción: La condición funcional física y psíquica son factores clave en la población anciana. Hay disponibles muchas herramientas de evaluación, pero no se pueden aplicar a la totalidad de la población geriátrica. Presentamos el test Alusti, cuyas 2versiones le permiten abarcar este amplio y complejo espectro de población. Material y métodos: Estudio prospectivo realizado con población institucionalizada, hospitalizada y comunitaria, desarrollado entre septiembre y diciembre del 2016. Se ha realizado el análisis comparativo con otros test: índice de Barthel (IB), velocidad de la marcha (VM), Timed «UP & GO» test (TUG), Short Physical Performance Battery (SPPB) y test Tinetti. Resultados: Se incluyeron 363 sujetos (edad media 83,25 años), con diferentes niveles de situación funcional y cognitiva. La aplicación del test resulta sencilla, rápida (entre 3 y 6min), con un 100% de aplicabilidad y usabilidad, con efecto suelo-techo amplio (0-100 puntos), con un coeficiente de correlación intraclase (CCI) que muestra alta fiabilidad interobservador (CCI = 0,99) y buena correlación en su versión completa con el IB (CCI = 0,86; intervalo de confianza [IC]: 0,82-0,88) y el test Tinetti (CCI = 0,76; IC 95%: 0,71-0,81) así como en la abreviada con IB (CCI = 0,71; IC 95%: 0,65-0,75) y con el test Tinetti (CCI = 0,90; IC 95%: 0,88-0,92). Permite medir la variación de la situación funcional, que en nuestra muestra ha generado un aumento del 10,9%, tras un periodo de hospitalización. Conclusiones: El test Alusti permite la evaluación del rendimiento físico en la totalidad de la población geriátrica. La mayor concordancia se produce con el test Tinetti, al que supera en aplicabilidad

Introduction: Physical and psychological functional conditions are key factors in the elderly population. Many evaluation tools are available, but they cannot be applied to the whole geriatric population. The use Alusti Test is presented. This test consists of 2versions, which enable it to encompass this wide and complex population spectrum. Materials and methods: A prospective study with the institutionalised, hospitalised, and community population, was conducted between September and December 2016. A comparative analysis was conducted using the Barthel Index (BI), Gait Speed Test (GST), Timed «Up & Go» Test (TUG), Short Physical Performance Battery (SPPB), and Tinetti Test. Results: A total of 363 subjects were enrolled (mean age: 83.25 years), with varying levels of functional and cognitive conditions. The test was simple and quick to apply (3-6min), 100% applicable and usable with broad floor and ceiling effects (0-100 points) with an intraclass correlation coefficient (ICC) that shows a high inter-observer reliability (ICC = 0.99), and a good correlation in its full version with BI (ICC = 0.86) (95% CI: 0.82-0.88), and the Tinetti test (ICC = 0.76; 95% CI: 0.71-0.81), as well as in the abbreviated version BI (ICC = 0.71; 95% CI: 0.65-0.75) and Tinetti Test (ICC = 0.90; 95% CI: 0.88-0.92). This allows the variation of the functional condition to be measured, which in our sample showed an increase of 10.9%, after a period of hospital admission. Conclusions: It is considered that Alusti test meets the requirements for physical performance assessment in the whole the geriatric population. The highest level of accuracy is given by the Tinetti test, which has greater applicability

Effect of Adding McKenzie Syndrome, Centralization, Directional Preference, and Psychosocial Classification Variables to a Risk-Adjusted Model Predicting Functional Status Outcomes for Patients With Lumbar Impairments.

Study Design Retrospective cohort. Background Patient-classification subgroupings may be important prognostic factors explaining outcomes. Objectives To determine effects of adding classification variables (McKenzie syndrome and pain patterns, including centralization and directional preference; Symptom Checklist Back Pain Prediction Model [SCL BPPM]; and the Fear-Avoidance Beliefs Questionnaire subscales of work and physical activity) to a baseline risk-adjusted model predicting functional status (FS) outcomes. Methods Consecutive patients completed a battery of questionnaires that gathered information on 11 risk-adjustment variables. Physical therapists trained in Mechanical Diagnosis and Therapy methods classified each patient by McKenzie syndromes and pain pattern. Functional status was assessed at discharge by patient-reported outcomes. Only patients with complete data were included. Risk of selection bias was assessed. Prediction of discharge FS was assessed using linear stepwise regression models, allowing 13 variables to enter the model. Significant variables were retained in subsequent models. Model power (R(2)) and beta coefficients for model variables were estimated. Results Two thousand sixty-six patients with lumbar impairments were evaluated. Of those, 994 (48%), 10 (<1%), and 601 (29%) were excluded due to incomplete psychosocial data, McKenzie classification data, and missing FS at discharge, respectively. The final sample for analyses was 723 (35%). Overall R(2) for the baseline prediction FS model was 0.40. Adding classification variables to the baseline model did not result in significant increases in R(2). McKenzie syndrome or pain pattern explained 2.8% and 3.0% of the variance, respectively. When pain pattern and SCL BPPM were added simultaneously, overall model R(2) increased to 0.44. Although none of these increases in R(2) were significant, some classification variables were stronger predictors compared with some other variables included in the baseline model. Conclusion The small added prognostic capabilities identified when combining McKenzie or pain-pattern classifications with the SCL BPPM classification did not significantly improve prediction of FS outcomes in this study. Additional research is warranted to investigate the importance of classification variables compared with those used in the baseline model to maximize predictive power. Level of Evidence Prognosis, level 4. J Orthop Sports Phys Ther 2016;46(9):726-741. Epub 31 Jul 2016. doi:10.2519/jospt.2016.6266.

World Health Organization cardiovascular risk stratification and target organ damage / Estratificación del riesgo cardiovascular de la Organización Mundial de la Salud y daño en órgano blanco

Background: Prediction charts allow treatment to be targeted according to simple markers of cardiovascular risk; many algorithms do not recommend screening asymptomatic target organ damage which could change dramatically the assessment. Objective: To demonstrate that target organ damage is present in low cardiovascular risk hypertensive patients and it is more frequent and severe as global cardiovascular risk increases. Methods: Consecutive hypertensive patients treated at a single Latin American center. Cardiovascular risk stratified according to 2013 WHO/ISH risk prediction chart America B. Left ventricular mass assessed by Devereux method, left ventricular hypertrophy considered >95 g/m2 in women and >115 g/m2 in men. Transmitral diastolic peak early flow velocity to average septal/lateral peak early diastolic relaxation velocity (E/e’ ratio) measured cut off value >13. Systolic function assessed by tissue Doppler average interventricular septum/lateral wall mitral annulus rate systolic excursion (s wave). Results: A total of 292 patients were included of whom 159 patients (54.5%) had cardiovascular risk of < 10%, 90 (30.8%) had cardiovascular risk of 10-20% and 43 (14.7%) had cardiovascular risk of >20%. Left ventricular hypertrophy was detected in 17.6% low risk patients, 27.8% in medium risk and 23.3% in high risk (p < 0.05), abnormal E/e′ ratio was found in 13.8%, 31.1% and 27.9%, respectively (p < 0.05). Mean s wave was 8.03 + 8, 8.1 + 9 and 8.7 + 1 cm/s for low, intermediate and high risk patients, respectively (p < 0.025). Conclusions: Target organ damage is more frequent and severe in high risk; one over four subjects was misclassified due to the presence of asymptomatic target organ damage

Antecedentes: Las tablas de riesgo cardiovascular dirigen el tratamiento según marcadores clínicos sencillos. Muchos algoritmos no recomiendan el cribado rutinario del daño de órgano blanco asintomático que podría cambiar drásticamente la estratificación. Objetivos: Demostrar que el daño en órgano blanco es altamente prevalente en el bajo riesgo cardiovascular y más frecuente y severo en la medida en que este aumenta. Material y métodos: Un total de 292 pacientes hipertensos consecutivos no tratados en un único centro latinoamericano. Riesgo cardiovascular estratificado según Guía 2013 OMS/ISH América B. Masa ventricular izquierda evaluada por método de Devereux, hipertrofia ventricular izquierda >95 g/m2 mujeres y >115 g/m2hombres. Se midió relación velocidad pico diastólico transmitral con doppler y velocidad diastólica precoz septal y lateral del anillo mitral con doppler tisular (relación E/e′), valor de corte >13. Función sistólica evaluada por doppler tisular como tasa de excursión tabique interventricular y pared lateral (onda s). Resultados: Un total de 159 pacientes (54,5%) presentaron riesgo cardiovascular <10%; 90 (30,8%) riesgo cardiovascular entre el 10% y el <20% y 43 (14,7%) presentaron un riesgo cardiovascular >20%. La hipertrofia ventricular izquierda en 17,6% pacientes fue de bajo riesgo, en el 27,8% de riesgo intermedio y en el 23,3% de alto riesgo (p < 0,05), con relación E/e′ anormal 13,8; 31,1 y 27,9%, respectivamente (p < 0,05). La onda s promedio fue de 8,03 + 8; 8,1 + 9 ; y 8,7 + 1 cm/seg para riesgo bajo, intermedio y alto, respectivamente (p < 0,025). Conclusiones: El daño en órgano blanco fue más frecuente y severo en alto riesgo; uno de cada 4 sujetos fue clasificado erróneamente debido a presencia de daño en órgano blanco subclínico

Is there one measure-of-fit that fits all? A taxonomy and review of measures-of-fit for risk-equalization models.

This study provides a taxonomy of measures-of-fit that have been used for evaluating risk-equalization models since 2000 and discusses important properties of these measures, including variations in analytic method. It is important to consider the properties of measures-of-fit and variations in analytic method, because they influence the outcomes of evaluations that eventually serve as a basis for policymaking. Analysis of 81 eligible studies resulted in the identification of 71 unique measures that were divided into 3 categories based on treatment of the prediction error: measured based on squared errors, untransformed errors, and absolute errors. We conclude that no single measure-of-fit is best across situations. The choice of a measure depends on preferences about the treatment of the prediction error and the analytic method. If the objective is measuring financial incentives for risk selection, the only adequate evaluation method is to assess the predictive performance for non-random groups.

Payment and provider profiling of episodes of illness of clinical illnesses involving rehabilitation.

The prevailing trend in American health care finance for the last two decades and likely for the forseeable future, is the movement from a system based on fee for service payments to one dominated by capitation arragements. At the core of this change is a shifting of risk from payers to providers. Such a fundamental transition is not without its difficulties. This is exemplified by some of the problems experienced by the Health Care Financing Administration (HCFA) as it has begun to encourage beneficiaries to move from traditional fee for service Medicare to capitated HMOs. The most recently published regulations involving risk adjustment of rates of payment for managed care organizations (MCOs) did not find much favor as the statistical power was poor and the clinical meaningfulness of the risk adjustment was highly problematic. Specifically, the risk adjustment explained less than 10% of the variation in costs (compared to approximately 30% when DRGs were first implemented). From a clinical perspective, the methodology only used hospitalization data for adjustment of capitation rates; that is, anyone who was not hospitalized was assumed to be healthy! It is known that payment for rehabilitation services is among the most difficult to understand and predict of all medical care. This article will summarize the development of a new risk adjustment methodology which should be particularly useful for payment and monitoring of episodes of clinical conditions that involve rehabilitation. The article will conclude with directions for future research.

The prospective risk adjustment system.

The Episode Classification System is intended to perform two tasks. First, it will be a prospective capitation risk adjuster and predict future health care costs. It will do this by assigning each individual a single capitation risk adjustment category based on an analysis of the medical history and of health care services rendered during a specific period of time. Second, the Episode Classification System will create retrospective severity adjusted Episodes of illness or Episodes of Care. These latter Episodes will provide a framework for relating patient characteristics to the amount, type, and duration of services provided during the treatment of a specific disease. These Episodes will give users the ability to understand past costs and the risk of mortality. As such they will form the basis for provider profiling by allowing users to analyze a complete clinical episode.

Medicare program: request for public comments on implementation of risk adjusted payment for the Medicare+Choice program and announcement of public meeting--HCFA. Solicitation of comments; announcement of meeting.

This notice solicits further public comments on issues related to the implementation of risk adjusted payment of Medicare+Choice organizations. Section 1853(a)(3) of the Social Security Act (the Act) requires the Secretary to implement a risk adjustment methodology that accounts for variation in per capita costs based on health status and demographic factors for payments no later than January 1, 2000. The methodology is to apply uniformly to all Medicare+Choice plans. This notice outlines our proposed approach to implementing risk adjusted payment. In order to carry out risk adjustment, section 1853(a)(3) of the Act also requires Medicare+Choice organizations, as well as other organizations with risk sharing contracts, to submit encounter data. Inpatient hospital data are required for discharges on or after July 1, 1997. Other data, as the Secretary deems necessary, may be required beginning July 1998. The Medicare+Choice interim final rule published on June 26, 1998 (63 FR 34968) describes the general process for the collection of encounter data. We also included a schedule for the collection of additional encounter data. Physician, outpatient hospital, skilled nursing facility, and home health data will be collected no earlier than October 1, 1999, and all other data we deem necessary no earlier than October 1, 2000. Given any start date, comprehensive risk adjustment will be made about three years after the year of initial collection of outpatient hospital and physician encounter data. Comments on the process for encounter data collection are requested in that interim final rule. We intend to consider comments received in response to this solicitation as we develop the final methodology for implementation of risk adjustment. This notice also informs the public of a meeting on September 17, 1998, to discuss risk adjustment and the collection of encounter data. The meeting will be held at the Health Care Financing Administration headquarters, located at 7500 Security Boulevard, Baltimore, MD, beginning at 8:30 a.m. Additional materials on the risk adjustment model will be available on or after October 15, 1998, and may be requested in writing from Chapin Wilson, Health Care Financing Administration, Department of Health and Human Services, 200 Independence Avenue, S. W., Room 435-H, Washington, DC 20201.