Background
Decision aids are intended to help people participate in decisions that involve weighing the benefits and harms of treatment options often with scientific uncertainty.

Objectives
To assess the effects of decision aids for people facing treatment or screening decisions.

Search methods
For this update, we searched from 2009 to June 2012 in MEDLINE; CENTRAL; EMBASE; PsycINFO; and grey literature. Cumulatively, we have searched each database since its start date including CINAHL (to September 2008).

Selection criteria
We included published randomized controlled trials of decision aids, which are interventions designed to support patients' decision making by making explicit the decision, providing information about treatment or screening options and their associated outcomes, compared to usual care and/or alternative interventions. We excluded studies of participants making hypothetical decisions.

Data collection and analysis
Two review authors independently screened citations for inclusion, extracted data, and assessed risk of bias. The primary outcomes, based on the International Patient Decision Aid Standards (IPDAS), were:

A) 'choice made' attributes;

B) 'decision-making process' attributes.

Secondary outcomes were behavioral, health, and health-system effects. We pooled results using mean differences (MD) and relative risks (RR), applying a random-effects model.

Main results
This update includes 33 new studies for a total of 115 studies involving 34,444 participants. For risk of bias, selective outcome reporting and blinding of participants and personnel were mostly rated as unclear due to inadequate reporting. Based on 7 items, 8 of 115 studies had high risk of bias for 1 or 2 items each.

Of 115 included studies, 88 (76.5%) used at least one of the IPDAS effectiveness criteria: A) 'choice made' attributes criteria: knowledge scores (76 studies); accurate risk perceptions (25 studies); and informed value-based choice (20 studies); and B) 'decision-making process' attributes criteria: feeling informed (34 studies) and feeling clear about values (29 studies).

A) Criteria involving 'choice made' attributes:

Compared to usual care, decision aids increased knowledge (MD 13.34 out of 100; 95% confidence interval (CI) 11.17 to 15.51; n = 42). When more detailed decision aids were compared to simple decision aids, the relative improvement in knowledge was significant (MD 5.52 out of 100; 95% CI 3.90 to 7.15; n = 19). Exposure to a decision aid with expressed probabilities resulted in a higher proportion of people with accurate risk perceptions (RR 1.82; 95% CI 1.52 to 2.16; n = 19). Exposure to a decision aid with explicit values clarification resulted in a higher proportion of patients choosing an option congruent with their values (RR 1.51; 95% CI 1.17 to 1.96; n = 13).

B) Criteria involving 'decision-making process' attributes:

Decision aids compared to usual care interventions resulted in:

a) lower decisional conflict related to feeling uninformed (MD -7.26 of 100; 95% CI -9.73 to -4.78; n = 22) and feeling unclear about personal values (MD -6.09; 95% CI -8.50 to -3.67; n = 18);

b) reduced proportions of people who were passive in decision making (RR 0.66; 95% CI 0.53 to 0.81; n = 14); and

c) reduced proportions of people who remained undecided post-intervention (RR 0.59; 95% CI 0.47 to 0.72; n = 18).

Decision aids appeared to have a positive effect on patient-practitioner communication in all nine studies that measured this outcome. For satisfaction with the decision (n = 20), decision-making process (n = 17), and/or preparation for decision making (n = 3), those exposed to a decision aid were either more satisfied, or there was no difference between the decision aid versus comparison interventions. No studies evaluated decision-making process attributes for helping patients to recognize that a decision needs to be made, or understanding that values affect the choice.

C) Secondary outcomes

Exposure to decision aids compared to usual care reduced the number of people of choosing major elective invasive surgery in favour of more conservative options (RR 0.79; 95% CI 0.68 to 0.93; n = 15). Exposure to decision aids compared to usual care reduced the number of people choosing to have prostate-specific antigen screening (RR 0.87; 95% CI 0.77 to 0.98; n = 9). When detailed compared to simple decision aids were used, fewer people chose menopausal hormone therapy (RR 0.73; 95% CI 0.55 to 0.98; n = 3). For other decisions, the effect on choices was variable.

The effect of decision aids on length of consultation varied from 8 minutes shorter to 23 minutes longer (median 2.55 minutes longer) with 2 studies indicating statistically-significantly longer, 1 study shorter, and 6 studies reporting no difference in consultation length. Groups of patients receiving decision aids do not appear to differ from comparison groups in terms of anxiety (n = 30), general health outcomes (n = 11), and condition-specific health outcomes (n = 11). The effects of decision aids on other outcomes (adherence to the decision, costs/resource use) were inconclusive.

Authors' conclusions
There is high-quality evidence that decision aids compared to usual care improve people's knowledge regarding options, and reduce their decisional conflict related to feeling uninformed and unclear about their personal values. There is moderate-quality evidence that decision aids compared to usual care stimulate people to take a more active role in decision making, and improve accurate risk perceptions when probabilities are included in decision aids, compared to not being included. There is low-quality evidence that decision aids improve congruence between the chosen option and the patient's values.

New for this updated review is further evidence indicating more informed, values-based choices, and improved patient-practitioner communication. There is a variable effect of decision aids on length of consultation. Consistent with findings from the previous review, decision aids have a variable effect on choices. They reduce the number of people choosing discretionary surgery and have no apparent adverse effects on health outcomes or satisfaction. The effects on adherence with the chosen option, cost-effectiveness, use with lower literacy populations, and level of detail needed in decision aids need further evaluation. Little is known about the degree of detail that decision aids need in order to have a positive effect on attributes of the choice made, or the decision-making process.

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Decision aids for people facing health treatment or screening decisions

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

https://www.chpso.org/sites/main/files/file-attachments/chest-2008-geerts-381s-453s.pdf

Prevention of Venous Thromboembolism* American College of Chest Physicians Evidence- Based Clinical Practice Guidelines (8th Edition)

Importance The coronavirus disease 2019 (COVID-19) pandemic, due to the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a worldwide sudden and substantial increase in hospitalizations for pneumonia with multiorgan disease. This review discusses current evidence regarding the pathophysiology, transmission, diagnosis, and management of COVID-19. Observations SARS-CoV-2 is spread primarily via respiratory droplets during close face-to-face contact. Infection can be spread by asymptomatic, presymptomatic, and symptomatic carriers. The average time from exposure to symptom onset is 5 days, and 97.5% of people who develop symptoms do so within 11.5 days. The most common symptoms are fever, dry cough, and shortness of breath. Radiographic and laboratory abnormalities, such as lymphopenia and elevated lactate dehydrogenase, are common, but nonspecific. Diagnosis is made by detection of SARS-CoV-2 via reverse transcription polymerase chain reaction testing, although false-negative test results may occur in up to 20% to 67% of patients; however, this is dependent on the quality and timing of testing. Manifestations of COVID-19 include asymptomatic carriers and fulminant disease characterized by sepsis and acute respiratory failure. Approximately 5% of patients with COVID-19, and 20% of those hospitalized, experience severe symptoms necessitating intensive care. More than 75% of patients hospitalized with COVID-19 require supplemental oxygen. Treatment for individuals with COVID-19 includes best practices for supportive management of acute hypoxic respiratory failure. Emerging data indicate that dexamethasone therapy reduces 28-day mortality in patients requiring supplemental oxygen compared with usual care (21.6% vs 24.6%; age-adjusted rate ratio, 0.83 [95% CI, 0.74-0.92]) and that remdesivir improves time to recovery (hospital discharge or no supplemental oxygen requirement) from 15 to 11 days. In a randomized trial of 103 patients with COVID-19, convalescent plasma did not shorten time to recovery. Ongoing trials are testing antiviral therapies, immune modulators, and anticoagulants. The case-fatality rate for COVID-19 varies markedly by age, ranging from 0.3 deaths per 1000 cases among patients aged 5 to 17 years to 304.9 deaths per 1000 cases among patients aged 85 years or older in the US. Among patients hospitalized in the intensive care unit, the case fatality is up to 40%. At least 120 SARS-CoV-2 vaccines are under development. Until an effective vaccine is available, the primary methods to reduce spread are face masks, social distancing, and contact tracing. Monoclonal antibodies and hyperimmune globulin may provide additional preventive strategies. Conclusions and Relevance As of July 1, 2020, more than 10 million people worldwide had been infected with SARS-CoV-2. Many aspects of transmission, infection, and treatment remain unclear. Advances in prevention and effective management of COVID-19 will require basic and clinical investigation and public health and clinical interventions.

/pdf/pathophysiology-transmission-diagnosis-and-treatment-of-2o6dok06xg.pdf

Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review.

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.

/pdf/transparent-reporting-of-a-multivariable-prediction-model-3n8wy14hp9.pdf

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration.

A 67-year-old man makes his annual visit to a physician in general medicine practice. The patient says that he feels well, but he is worried about the possibility that he may have an abdominal aortic aneurysm (AAA). Recently, his close friend and partner of over 30 years died suddenly from an AAA rupture. Like the patient, his friend was in his late 60s and had a history of mild hypertension and hyperlipidemia. ‘‘If my friend could get an aneurysm, so could I,’’ the patient says. The physician, aware that the patient’s hypertension and hyperlipidemia have been adequately controlled with a diuretic and a statin, that he has no other medical or family

Deciding whether to screen patients for abdominal aortic aneurysm

A ccelerating the development of clinical research in academic hospitalist programs is a worthwhile goal if pursued with clarity, objectivity, and a thorough understanding of the process and its implications. In their articles, Flanders et al. and Wright et al. identify major barriers to growing academic hospitalist programs. These barriers include the need for protected time, the shortage of trained research faculty, the lack of infrastructure, and the limited availability of senior mentors. Both Flanders et al. and Wright et al. offer smart and innovative ways of addressing these issues. However, building an academic program from the ground up is more complex and challenging than it may seem at first glance. It takes time, patience, creativity, diplomacy, and the ability to recruit collaborators and advocates who are willing to share infrastructure and resources. Although both articles add significantly to the discussion of strategies for creating an academic hospitalist program, they are unclear about the definition of academic in this context. The term academic is often misunderstood to be synonymous with research. However, research is just one component of an academic program, which also includes education, quality improvement (QI), administration, and program development. It may be helpful, therefore, to replace academic with scholarship, which can be defined as a process that involves peer review and dissemination of ideas at local, regional, and national levels. Scholarship also goes beyond research, encompassing education and other areas such as QI. Although academic programs are not necessarily involved with funded research, there is usually an expectation of peer review, through either presentations at regional and national meetings or publication. For the purposes of this discussion, the term academic hospitalist program will be defined broadly to include any program affiliated with a university that is involved in the teaching of residents and medical students and whose faculty is required to participate in a promotions process. All members of an academic division should be expected to participate in scholarship, whether it is education, QI projects, or research. If there is a strong expectation that traditional National Institute of Health (NIH) funded research will take place, this expectation must come with sufficient resources. Without infrastructure for research and investment in research faculty, procuring NIH funds for research is not a reasonable expectation. Organizers of hospitalist programs currently within academic divisions of general internal medicine should consider ways to better integrate programs into the existing research infrastructure in their divisions. For either freestanding hospitalist programs or programs within academic divisions of general internal medicine, investments in infrastructure and faculty are

/pdf/helping-hospitalists-achieve-academic-stature-5bskagwk0t.pdf

Helping hospitalists achieve academic stature.

Flanders and colleagues have written an important opinion piece that helps advance the critical dialogue regarding the future organization of academic divisions of general internal medicine (ADGIM).1 The authors give a balanced view of the current state of ADGIM and some of the challenges now faced as hospitalist programs rapidly grow. According to Flanders, the demand for hospitalists continues to grow for all hospital types. Several pressures have fueled this rapid growth: shrinking profit margins in the health industry, hospitals’ financial need to decrease LOS, increasing quality measures imposed by the federal government and insurers, and the recent development of work-duty regulations, which have stimulated teaching hospitals to create non-housestaff inpatient coverage. The latter factor has pushed academic medical centers to rapidly hire hospitalists, both for patient care and education.

It is important to understand the focus of the discussion and to appreciate that the definition of a hospitalist has varied and changed over the years from any physician who spends two to four months on service to someone who spends more than 5 months on inpatient service and has no outpatient responsibilities. Confusion occurs when trying to define an academic hospitalist and compare and contrast duties and expectations when roles and definitions vary dramatically from institution to institution.

As hospitalist programs in academic medical centers grow, a debate is intensifying: where should these programs be housed? The vast majority of hospitalists are general internists, and most academic hospitalist programs currently reside within divisions of general internal medicine. Should hospitalists develop their own divisions or departments, or should they remain within ADGIM? Flanders and colleagues articulate two arguments for maintaining the status quo. First and foremost, they say, hospitalist programs currently lack adequate academic development and infrastructure, making it impossible for them to build or create a dedicated academic division. This argument is valid if we define “academic” broadly, i.e., encompassing research as well as education.

In 2008, each academic unit should strive to become a “triple threat.”2 Most academic hospitalist programs have extensive clinical expectations and educational responsibilities, but few have research strength. Because academic hospital medicine is still maturing, most academic hospitalists are assistant professors. Most lack training in educational scholarship and formal clinical research. Moreover the lack of associate professors and full professors makes mentorship challenging. The lack of research experience and infrastructure is exacerbated by NIH budget tightening: even established investigators are struggling to retain funding.

We agree that the lack of academic maturity should influence whether hospitalists should be housed in separate divisions. This, however, implies that if only hospitalist medicine had access to research infrastructure and mentors, then separating it out from DGIM would work. In other words, the authors imply that the only problem is bad timing; everything else supports creation of a separate hospitalist division. This indirectly implies and assumes that geography determines a specialty. We disagree with that premise. Consider cardiology (whether invasive, non-invasive, imaging, EP, etc.) or pulmonary medicine (ICU critical care, asthma care, sleep labs, etc.). In both cases, the argument that geography justifies splitting off a part of division to create new divisions would be analogous to that of hospitalist medicine splitting off from internal medicine. Yet they have not done so. This demonstrates that the problems with a separate division of hospital medicine extend beyond the lack of mentors and research infrastructure that are discussed in the article. Splintering a group and widening a divide between inpatient general internists and outpatient general internists could jeopardize and diffuse training, increase health care costs because of heightened redundancy of administrative support, and, most seriously, threaten quality of care, especially in terms of continuity, in major teaching hospitals. Many academic general internists continue to split their time between inpatient and outpatient medicine. They can manage this because their clinics are in the hospital, where their patients, who tend to be sicker, are frequently admitted. General internists manage this work efficiently and provide high-quality care. Where would they fit in in a split system?

The current hospital focus on decreasing lengths of stay leads to patients leaving hospitals “quicker and sicker.” In addition, the growing wave of aging baby boomers has forced general internists in ambulatory care practices, especially those in academic medical centers, to care for more and more complex patients with multiple chronic illnesses, often known by the term “tertiary primary care.” The movement toward creating a “medical home,” which is being piloted by commercial insurers and Medicare, emphasizes the need for smooth transitions between hospitals and primary care sites. Patients will benefit from inpatient and outpatient internists working closely to expedite transitions of care, potentially reducing length of stay and reducing non-reimbursed readmission of patients not adequately followed after discharge.

Internists have the advantage of a patient perspective rather than a disease perspective. This advantage exists regardless of our practice site. We consider hospitalists, outpatient internists and dual-site internists as all patient-centered physicians. While we adopt different practice skills, we share a common orientation. We believe that internal medicine training will benefit from the different flavors of general internists working together to define education, clinical practice and research issues. For those reasons, we hope that the majority of academic hospitalists remain in academic divisions of general internal medicine.

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Where Should Hospitalist Programs be Housed

Excerpt To examine the problems of chronic liver disease in narcotics users, liver sections of consecutive addicts who were autopsied at the Medical Examiner's Office (none died of liver disease) w...

Thomas McGinn

Papers

Deciding whether to screen patients for abdominal aortic aneurysm

Helping hospitalists achieve academic stature.

Where Should Hospitalist Programs be Housed

Studies of Chronic Liver Disease in Narcotic Addicts.

Clinical prediction rules for hospitalists