Smoking cessation efforts may reduce avoidable hospital readmissions

Patients with lung blood clots who smoke are more likely to be readmitted for lung blood clots than nonsmokers, according to Kam Sing Ho, MD, from Mount Sinai St. Luke's and Mount Sinai West in New York City, who will present the study findings at the CHEST Annual Meeting 2019 in New Orleans.

The findings may be relevant for hospitals and health systems as they try to curb unnecessary readmissions. If a provider underperforms compared to peer hospitals when it comes to avoidable readmissions, the Centers for Medicare and Medicaid Services withholds reimbursement as a financial penalty.

Smoking cessation efforts, from the health system, the community or the patients themselves, could ultimately be a strong investment that allows the hospital to retain more reimbursement dollars.

WHAT'S THE IMPACT

Pulmonary emboli (PE), or blood clots in the lung, are common and are associated with 100,000 deaths annually. Risk factors include advanced age, surgery, trauma, prolonged immobility, cancer, pregnancy, estrogen therapy, congestive heart failure and defects in blood coagulation factors.

The researchers conducted a retrospective study using the AHRQ-HCUP Nationwide Readmission Database to study the role of smoking in hospital readmissions with a primary diagnosis of pulmonary embolism and a secondary diagnosis of tobacco dependence, also called active smoking.

Active smokers have a readmission rate for PE at 34.2%, higher than that of non-smokers. About 11% of smokers and 9% of non-smokers with PE were readmitted within 30 days, and PE was the main cause for readmission in 13% of those cases, the most prevalent independent factor associated with the higher readmission rate.

The investigators found that readmitted patients were twice as likely to die than patients on their first admission (6.27 % vs. 3.16%). Smoking was associated with readmission as were female gender, atrial fibrillation, in-hospital oxygen requirement, Medicare insurance and additional illnesses. Private insurance and higher income status were associated with fewer readmissions.

THE LARGER TREND

Readmissions occur for almost 20 percent of patients hospitalized in the U.S. and are associated with patient harm and expenses. Rates of unplanned readmission within 30 days after discharge are used to benchmark a hospital's performance and quality of patient care. Yet clinicians are often poorly equipped to identify patients who will be readmitted, and many readmissions are thought to be preventable.

A novel machine learning model developed at the University of Maryland Medical System, called the Baltimore score (or B score), may help hospitals better predict which discharged patients are likely to be readmitted, a University of Maryland School of Medicine study found in June.

Existing readmission risk-assessment tools, including the LACE index, the HOSPITAL score and the Maxim/RightCare score, look at a limited set of variables for each patient, such as length of stay in a hospital, type and severity of admission, types and amounts of medications, other chronic conditions a patient may have, and previous hospital admissions.
 

Twitter: @JELagasse

Email the writer: jeff.lagasse@himssmedia.com