The desire to reduce readmission after heart failure hospitalisation is not a new one.
Over 30% of hospitalisations for heart failure in Australia and New Zealand result in death or readmission within the 30-day window. One out of three. That’s pretty abysmal odds.
Unfortunately, there are few working solutions routinely offered outside of an ever-expanding series of drug-cocktails.
Factors Affecting Heart Failure Readmission Rates
Diet and Exercise
There are a few useful recommendations available which stress non-pharmacologic management of the heart failure (HF) client. For one, there is a need to educate and counsel patients about the importance of dietary restriction of salt and fluid management (water restriction). Failure to manage both dietary elements is the single most common cause of flare-ups in congestive heart failure patients.
Unfortunately, there is a cataclysmic disconnect between the provision of ‘good counsel’ and proper follow-through. When a patient receives stellar advice from a qualified professional, all that has occurred is an (often one-way) discussion.
It remains wholly possible for the patient to ignore, forget or otherwise dismiss every dietary pearl he received only to remember them when poised on the gurney’s edge. This is not necessarily willful disregard. Truthfully, often, the patient just has one too many things on his plate to allow for thoughtful dietary management.
The same is true for many other recommendations which can be made to decrease the risk of rehospitalisation. A strong case can be made for HF patients to seek proper exercise instruction, as regular activity has been shown to be of prime importance in the management of their disease.
“Wonderful!” cry the physios in the room.
Therapists were born to teach exercise, implement practice sessions, correct form and cheer-lead. Unfortunately, even a master clinician is unable to make a single person rise-up from the recliner once the therapist leaves the building.
This lack of willingness to participate in the joys of exercise may not actually be a lack of willingness to participate. It may be good-old garden variety depression. Depression may be 4 to 5 times as common in heart failure patients as in the population at large. Most patients with HF would benefit from a screening for depression.
The occupational therapist is the obvious choice for performing such an assessment. Whether in the hospital (patients who are hospitalised are at an elevated risk for depression) or the outpatient setting, the OT has a multitude of batteries at his fingertips.
So what about the proper management of medication? Guidelines make a compelling argument for having a qualified person routinely review and simplify medication regimes. Again, occupational therapists are positioned perfectly to help assess whether patients have the cognitive capacity to manage complex medication regimes and to alter these regimes to reduce any such complexities.
All of these recommendations – plus the amazingly useful pharmacological remedies available – can be helpful in the management of heart failure. Unfortunately, none of them address the 800 lb gorilla in the room: Why do these patients boomerang back to the hospital so quickly?
This may be a problem of paradigm rather than a failure of innovation. The healthcare system tends to identify “risk” as the sum total of meticulously collected data points culled from inpatient records and administrative data: age, gender, clinical signs, payer source, comorbidities and so on.
What is Missing From This Equation?
The missing factor seems to be a discussion of environment. Healthcare providers need to ask themselves: into what level of chaos is this patient being discharged?
Over the last few years, attention has – finally, properly – been shifting onto the importance that social instability can play in the revolving door syndrome of hospitalisation. Social instability is a term of convenience used to reﬂect ‘a relative lack of social support, education, economic stability, access to care, and safety in the patient’s environment.’ (For some, a shorter definition of social instability might be “life”.)
Environmental Social Instability
So how can these factors be quantified? As early as 2008, Arbajie and colleagues, made one of the first stabs at clarifying the predictive factor of environmental social instability. Their cohort study examined patient environments to find predictors that could signal a propensity for rehospitalisation.
They asked questions which – frankly – are second nature to the physiotherapist and occupational therapist:
- Does the patient have a regular medical provider?
- Does he require assistance to get to the doctor?
- Does he live alone?
- Is he married?
- Is the spouse healthy?
- Are there kids anywhere nearby?
- Will the kids – or anyone – help with activities of daily living?
- Are there basic functional needs (like showering or preparing meals) which are not met?
- Are there stairs inside the house that must be navigated?
Their conclusions were compelling. Once they adjusted their results for demographics, health and functional status, they were able to focus on which environmental conditions led to early rehospitalisation.
Patients who lived by themselves, who needed help with ADLs and did not get it, who lacked self-management skills, and who had limited education were all at higher risk of 30-day readmission. An interesting point of this analysis is the fact that after adjusting for these factors, there was no direct relationship between income and risk in this study.
Not all that ails the heart patient is visible… or physical. Therapists who ignore the nonphysical elements of CHF do so to their peril. A diagnosis of CHF – especially severe CHF –significantly reduces participation in instrumental, leisure, and social activity. The extent of restriction of participation, however, surprised even the people who first asked the question. For example, despite being younger, their study subjects had given up (on average) 20% more of their activities since being diagnosed with CHF as had a population of post-CVA patients.
Is it possible to come up with a predictive score for 30-day readmission or death in patients with heart failure?
A group of Australian researchers wanted to know if it was possible to create an “at risk” index which would allow targeting on resources on higher-risk patients so they developed a score for likelihood of heart failure leading to death or rebound hospitalisation within the 30-day window.
They looked at all kinds of patient data including the patient’s age, gender, marital status, home situation, remoteness index (where in Australia the person lived), presence or absence of insurance, and whether the patient received any home-based therapy.
When the researchers looked at the “typical” factors often included in such models, they came up with a poorly discriminative model. In other words, the model they thought would work did not do a good job predicting readmissions.
But the researchers found that when they added several atypical factors, not often included in models such as this, the predictive value improved dramatically. This model became much more predictive by adding testing of the heart (echocardiography), mental health status (including screening for depression), cognition, and individualised socioeconomic status to the screen.
In 2017, another Australian study examined whether heart failure patients with multiple comorbidities would be more likely to be readmitted after discharge. The answer was a resounding yes. Patients with diabetes, metabolic and mood disorders were the most likely to be readmitted, with patients with renal failure also showing an elevated risk.
The medical system tends to treat HF as a primarily physical problem with physical solutions. However, patients with CHF suffer from many “nonphysical” problems which fall into five broad domains: symptoms, role loss, effective response, coping, and social support.
Physiotherapists and occupational therapists can provide two sides of the rehabilitation coin for their patients with heart failure. Their ability to provide physical training, educational services, cognitive retraining, medication education, and depression screening make them a valuable asset in the ever-expanding quest to diminish hospital readmissions.
- Arbaje, AI, Wolff, JL, Yu, Q, Powe, NR, Anderson, GF & Boult, C 2008, ‘Postdischarge environmental and socioeconomic factors and the likelihood of early hospital readmission among community-dwelling Medicare beneficiaries’, The Gerontologist, vol. 48, no. 4, pp. 495–504, viewed 20 November, https://www.ncbi.nlm.nih.gov/pubmed/18728299
- Amarasingham, R, Moore, BJ, Tabak, YP, Drazner, MH, Clark, CA, Zhang, S, Reed, WG, Swanson, TS, Ma, Y & Halm, EA 2010, ‘An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data’, Medical care, vol. 48, no. 11, pp. 981–8, viewed 20 November 2017, https://www.ncbi.nlm.nih.gov/pubmed/20940649
- Foster, ER, Cunnane, KB, Edwards, DF, Morrison, MT, Ewald, GA, Geltman, EM & Zazulia, AR 2011, ‘Executive Dysfunction and Depressive Symptoms Associated With Reduced Participation of People With Severe Congestive Heart Failure’, American Journal of Occupational Therapy, vol. 65, no. 3, pp. 306–313, viewed 20 November 2017, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155250/
- Hersh, AM, Masoudi, FA & Allen, LA 2013, ‘Postdischarge environment following heart failure hospitalization: expanding the view of hospital readmission’, Journal of the American Heart Association, vol. 2, no. 2, viewed 20 November 2017, http://jaha.ahajournals.org/content/2/2/e000116.short
- Huynh, QL, Negishi, K., Blizzard, L, Sanderson, K, Venn, AJ & Marwick, TH 2016, ‘Predictive score for 30-day readmission or death in heart failure’, JAMA cardiology, vol. 1, no. 3, pp. 362-4, viewed 20 November 2017,
- Huynh, QL, Negishi, K, Blizzard, L, Saito, M, De Pasquale, CG, Hare, JL, Leung, D, Stanton, T, Sanderson, K, Venn, AJ & Marwick, TH 2016, ‘Mild cognitive impairment predicts death and readmission within 30days of discharge for heart failure’, International journal of cardiology, vol. 221, 99. 212-17, viewed 20 November 2017, http://www.sciencedirect.com/science/article/pii/S0167527316314346
- Institute for Clinical Systems Improvement 2013, ‘Heart failure in adults’, National Guideline Clearinghouse, viewed 20 November 2017, https://guideline.gov/summaries/summary/47030
- Joynt, KE,Whellan, DJ, & O’Connor, CM 2004, ‘Why is depression bad for the failing heart? A review of the mechanistic relationship between depression and heart failure’, Journal of Cardiac Failure, vol. 10, iss. 3, pp. 258–71 viewed 20 November 2017, http://www.sciencedirect.com/science/article/pii/S1071916403007176
- Labrosciano, C, Air, T, Tavella, R, Beltrame, J, Zeitz, C, Horton, D & Ranasinghe, I 2017, ‘Rates of 30-Day Readmission and Mortality After Heart Failure Hospitalisation in Australia and New Zealand: A Population Study’, Heart, Lung and Circulation, vol. 26.
- Tully, PJ, Baumeister, H, Bennetts, JS, Rice, GD & Baker, RA 2016, ‘Depression screening after cardiac surgery: a six month longitudinal follow up for cardiac events, hospital readmissions, quality of life and mental health’, International journal of cardiology, vol. 206, pp. 44-50, viewed 20 November 2017, http://www.sciencedirect.com/science/article/pii/S0167527316300274
- Wiley, JF, Chan, YK, Ahamed, Y, Ball, J, Carrington, MJ, Riegel, B & Stewart, S 2017, ‘Multimorbidity and the Risk of All-Cause 30-Day Readmission in the Setting of Multidisciplinary Management of Chronic Heart Failure: A Retrospective Analysis of 830 Hospitalized Patients in Australia’, The Journal of cardiovascular nursing, viewed 20 November 2017, http://europepmc.org/abstract/med/28107252