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Grading the strenth of body of Evidence / Researdh

using the two articles one on traumatic brain injury and the other on grading the strength of research with the following requirements.
Review Care of the patient with mild traumatic brain injury (PDF) (Links to an external site.) and AHRQ Series Paper 5: Grading the strength of a body of evidence (PDF) (Links to an external site.).
•Are the guidelines based on evidence?
•Using the Agency for Healthcare Research and Quality system, what is the evidence rating?
•Would you use the guideline based on the evidence provided
Is there a better approach?
•How does the evidence you select for your paper fit into this evidence rating? My paper is on CHF and readmission rates after 30 days. see attached PICO question
APA 12 font New Times Roman
There is not a certain page limit but just enough to answer the question. I do not need a title page. but I do need a reference page.
In 2010 in the United States, approximately 6.6 million adults older than 18 years had
HF.2 It is estimated that by 2030, an additional 3 million people will have HF, representing a 25.0% increase in prevalence (Butler, et al, 2012). To date, there has not been a single trial targeting acute HF that has been shown to improve survival or readmission risk in these
patients. Besides adhering to existing guidelines, newer approaches to managing these patients, both in terms of monitoring and developing novel therapeutic approaches, are desperately needed. These efforts are even more important now that readmission rates for
HF is now a quality measure and hospitalization within 30 days of discharge will not be reimbursed to the hospitals. Thus, efforts to reduce HF hospitalization are clinically important and represents a national aim (Butler, et al. 2012). Congest Heart Fail Vol. 18 | No. 5 Suppl. 1 | September. October 2012
About 5.7 million people in the United States have heart failure. The number of people who have this condition is growing.
Heart failure is more common in:
• People who are age 65 or older. Aging can weaken the heart muscle. Older people also may have had diseases for many years that led to heart failure. Heart failure is a leading cause of hospital stays among people on Medicare.
• Blacks are more likely to have heart failure than people of other races. They’re also more likely to have symptoms at a younger age, have more hospital visits due to heart failure, and die from heart failure.
• People who are overweight. Excess weight puts strain on the heart. Being overweight also increases your risk of heart disease and type 2 diabetes. These diseases can lead to heart failure.
• People who have had a heart attack. Damage to the heart muscle from a heart attack and can weaken the heart muscle http://www.nhlbi.nih.gov/health/health-topics/topics/hf/atrisk 2015.
The 2010 Patient Protection and Affordable Care Act (PPACA) aims to increase access to outpatient care and improve the quality of such care through implementation of evidence-based outpatient management systems and strategies
Section 3025 of the 2010 Affordable Care Act (Public Law 111-148) requires the Secretary of the Department of Health and Human Services to establish a Hospital Readmissions Reduction Program whereby the Secretary reduces Inpatient Prospective Payment System (IPPS) payments to hospitals for excess readmissions beginning on or after October 1, 2012 (Fiscal Year [FY] 2013). The Centers for Medicare & Medicaid Services (CMS) adopted and publicly reported the following 30-day risk standardized readmission measures to comply with these requirements.
About 5.7 million adults in the United States have heart failure.1 • One in 9 deaths in 2009 included heart failure as contributing cause.1 • About half of people who develop heart failure die within 5 years of diagnosis.1 • Heart failure costs the nation an estimated $30.7 billion each year.3 This total includes the cost of health care services, medications to treat heart failure, and missed days of work. CDC fact sheet http://www.cdc.gov/dhdsp/data_statistics/fact_sheets/docs/fs_heart_failure.pdf
Rehospitalization for Heart Failure
Predict or Prevent?
Akshay S. Desai and Lynne W. Stevenson
Download PDF
http://dx.doi.org/10.1161/CIRCULATIONAHA.112.125435 Published: July 24, 2012
Heart failure is the leading cause of hospitalization among adults >65 years of age in the United States. Annually, >1 million patients are hospitalized with a primary diagnosis of heart failure, accounting for a total Medicare expenditure exceeding $17 billion
Readmission rates are also higher when psychosocial and/or socioeconomic factors limit adherence and compliance with medications, self-monitoring, and follow-up
Models of HF care. The first panel reflects the traditional model of the ambulatory heart failure clinic as a focal point for intermittent assessment and chronic heart failure management. The second panel reflects a reengineered ambulatory heart failure treatment center with tighter linkage to home surveillance and options for active treatment as an alternative to hospitalization. HF indicates heart failure; ED, emergency department.
Background— In 2009, the Centers for Medicare & Medicaid Services is publicly reporting hospital-level risk-standardized 30-day mortality and readmission rates after acute myocardial infarction (AMI) and heart failure (HF). We provide patterns of hospital performance, based on these measures.
Methods and Results— We calculated the 30-day mortality and readmission rates for all Medicare fee-for-service beneficiaries ages 65 years or older with a primary diagnosis of AMI or HF, discharged between July 2005 and June 2008. We compared weighted risk-standardized mortality and readmission rates across Hospital Referral Regions and hospital structural characteristics. The median 30-day mortality rate was 16.6% for AMI (range, 10.9% to 24.9%; 25th to 75th percentile, 15.8% to 17.4%; 10th to 90th percentile, 14.7% to 18.4%) and 11.1% for HF (range, 6.6% to 19.8%; 25th to 75th percentile, 10.3% to 12.0%; 10th to 90th percentile, 9.4% to 13.1%). The median 30-day readmission rate was 19.9% for AMI (range, 15.3% to 29.4%; 25th to 75th percentile, 19.5% to 20.4%; 10th to 90th percentile, 18.8% to 21.1%) and 24.4% for HF (range, 15.9% to 34.4%; 25th to 75th percentile, 23.4% to 25.6%; 10th to 90th percentile, 22.3% to 27.0%). We observed geographic differences in performance across the country. Although there were some differences in average performance by hospital characteristics, there were high and low hospital performers among all types of hospitals.
Conclusions— In a recent 3-year period, 30-day risk-standardized mortality rates for AMI and HF varied among hospitals and across the country. The readmission rates were particularly high
http://circoutcomes.ahajournals.org/content/2/5/407?ijkey=b2b398470ff2830be1e3277704a039588b6b759b&keytype2=tf_ipsecsha# Circulation
July 24, 2012, Volume 126, Issue 4
ACO #10 – Prevention Quality Indicator (PQI): Ambulatory Care Sensitive Conditions: Admissions for Heart Failure (HF)
Measure Information Form (MIF)
Data Source Medicare Part B Carrier claims data • Medicare Outpatient claims data • Medicare Inpatient claims data • Medicare beneficiary enrollment data
Measure Set ID • ACO #10
Version Number and effective date • Version 2.11 effective 1/1/15
CMS approval date • 11/19/20
NQF ID • #277, adapted for quality measurement in Medicare Accountable Care Organizations
Date Endorsed • N/A
Care Setting • Hospital
Unit of Measurement • Accountable Care Organization (ACO)
Measurement Duration • Calendar Year
Measurement Period • Calendar Year
Measure Type • Outcome
A Blueprint for the CMS Measures Management System, Version 9 Page 1 Health Services Advisory Group, Inc.
Measure Development
Measure Scoring • Prevention quality indicator (PQI) score, that is a ratio of observed admissions to expected admissions for Heart Failure (HF)
Payer source • Medicare Fee-for-Service
Improvement notation • Lower PQI scores are better
Measure steward • Agency for Healthcare Research and Quality (AHRQ) with adaptations by Centers for Medicare and Medicaid Services (CMS) (co-stewards).
Copyright / Disclaimer • This Medicare ACO PQI HF quality measure is adapted from the general population PQI quality measure for HF that is developed by AHRQ (AHRQ, 2013).
Measure description • All discharges with an ICD-9 or ICD-10 principal diagnosis code for HF in adults ages 18 years and older, for ACO assigned (SSP) or aligned (Pioneer) Medicare beneficiaries with HF, with risk-adjusted comparison of observed discharges to expected discharges for each ACO.
Rationale Hospital admissions for HF are a Prevention Quality Indicator of interest to comprehensive health care delivery systems, including ACOs. HF can often be controlled in an outpatient setting. Evidence suggests that these hospital admissions could have been avoided through high quality outpatient care, or the condition would have been less severe if treated early and appropriately. Proper outpatient treatment and adherence to care may reduce the rate of occurrence for this event, and thus of hospital admissions. Outpatient interventions such as the use of protocols for ambulatory management of low-severity patients and improvement of access to outpatient care would most likely decrease inpatient admissions for HF. In addition, physician management of patients with HF differs significantly by physician specialty (Edep, 1997; Reis, 1997). Such differences in practice may be reflected in differences in HF admission rates. Clinical Recommendation Statement Billings et al. (1993) found that low-income ZIP codes in New York City had 4.6 times more HF hospitalizations per capita than high-income ZIP codes. Millman (1993) reported that low-income ZIP codes had 6.1 times more HF hospitalizations per capita than high-income ZIP codes. Based on empirical results, areas with high rates of HF admissions also tend to have high rates of admissions for other Ambulatory Care Sensitive Conditions. The signal ratio (i.e., the proportion of the total variation across areas that is truly related to systematic differences in area performance rather than random variation) is very high, at 93.0 percent, indicating that the observed differences in age-sex adjusted rates very likely represent true differences across areas (AHRQ, 2007). Risk adjustment for age and sex exerts
A Blueprint for the CMS Measures Management System, Version 9 Page 2 Health Services Advisory Group, Inc. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/sharedsavingsprogram/Downloads/ACO-10.pdf

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