ANAHEIM, CALIF. – Variability in fasting lipid levels over time in statin-treated patients is of prognostic importance, David D. Waters, MD, reported at the American Heart Association scientific sessions.

More specifically, above-average visit-to-visit variability in fasting triglycerides, LDL cholesterol, or HDL cholesterol in atorvastatin-treated patients with known coronary artery disease proved to be a strong and independent predictor of coronary and cardiovascular events in a post hoc analysis of the landmark Treating to New Targets (TNT) trial (N Engl J Med 2005;352:1425-35).

Bruce Jancin/Frontline Medical News

[email protected]

ANAHEIM, CALIF. – Variability in fasting lipid levels over time in statin-treated patients is of prognostic importance, David D. Waters, MD, reported at the American Heart Association scientific sessions.

More specifically, above-average visit-to-visit variability in fasting triglycerides, LDL cholesterol, or HDL cholesterol in atorvastatin-treated patients with known coronary artery disease proved to be a strong and independent predictor of coronary and cardiovascular events in a post hoc analysis of the landmark Treating to New Targets (TNT) trial (N Engl J Med 2005;352:1425-35).

Bruce Jancin/Frontline Medical News

[email protected]

ANAHEIM, CALIF. – Variability in fasting lipid levels over time in statin-treated patients is of prognostic importance, David D. Waters, MD, reported at the American Heart Association scientific sessions.

More specifically, above-average visit-to-visit variability in fasting triglycerides, LDL cholesterol, or HDL cholesterol in atorvastatin-treated patients with known coronary artery disease proved to be a strong and independent predictor of coronary and cardiovascular events in a post hoc analysis of the landmark Treating to New Targets (TNT) trial (N Engl J Med 2005;352:1425-35).

Bruce Jancin/Frontline Medical News

[email protected]

The Food and Drug Administration is requiring a boxed warning on the label for obeticholic acid (Ocaliva) to highlight the correct weekly dosing regimen after incorrect daily dosing caused severe liver injury in patients with moderate to severe primary biliary cholangitis (PBC).

“FDA is adding a new Boxed Warning, FDA’s most prominent warning, to highlight this information in the prescribing information of the drug label,” FDA officials said in a statement Feb. 1. “To ensure correct dosing and reduce the risk of liver problems, FDA is clarifying the current recommendations for screening, dosing, monitoring, and managing PBC patients with moderate to severe liver disease taking Ocaliva.”

[email protected]

The Food and Drug Administration is requiring a boxed warning on the label for obeticholic acid (Ocaliva) to highlight the correct weekly dosing regimen after incorrect daily dosing caused severe liver injury in patients with moderate to severe primary biliary cholangitis (PBC).

“FDA is adding a new Boxed Warning, FDA’s most prominent warning, to highlight this information in the prescribing information of the drug label,” FDA officials said in a statement Feb. 1. “To ensure correct dosing and reduce the risk of liver problems, FDA is clarifying the current recommendations for screening, dosing, monitoring, and managing PBC patients with moderate to severe liver disease taking Ocaliva.”

[email protected]

The Food and Drug Administration is requiring a boxed warning on the label for obeticholic acid (Ocaliva) to highlight the correct weekly dosing regimen after incorrect daily dosing caused severe liver injury in patients with moderate to severe primary biliary cholangitis (PBC).

“FDA is adding a new Boxed Warning, FDA’s most prominent warning, to highlight this information in the prescribing information of the drug label,” FDA officials said in a statement Feb. 1. “To ensure correct dosing and reduce the risk of liver problems, FDA is clarifying the current recommendations for screening, dosing, monitoring, and managing PBC patients with moderate to severe liver disease taking Ocaliva.”

[email protected]

ABSTRACT

Proper reconstruction of proximal humeral anatomy is of primary importance to maximize patient outcomes after total shoulder arthroplasty. This article describes a new arthroplasty technique, where a fixed multiplanar bone resection is made and a novel implant, which is designed to precisely match the bone resection, is inserted. 

Continue to: The success of total shoulder arthroplasty...

The success of total shoulder arthroplasty (TSA) is largely dependent on how accurate the proximal humeral anatomy is reconstructed and the glenohumeral relationships are restored.^1-4 Numerous studies have demonstrated a relationship of worse clinical outcomes and implant failure with nonanatomic implant placement.^5-8 The majority of arthroplasty systems rely on surgeon-dependent decision-making to determine the location of the border of the articular surface and, ultimately, the amount and location of bone to be resected. Even in experienced hands, the ability to reproducibly restore the joint line is inconsistent.³

In contrast, the majority of total knee arthroplasty (TKA) systems have been designed with instrumentation that guides the surgeon precisely regarding where and how much femoral bone must be resected, and the corresponding implant is designed with the same thickness to preserve the location of the joint line. Cutting block instrumentation rather than freehand cuts enables reproducibility of TKA while being performed for an estimated 700,000 times annually in the US.⁹

To achieve similar high levels of reproducibility in shoulder arthroplasty, a new technique was developed based on the principle of providing instrumentation to assist the surgeon in accurately restoring the proximal humeral joint line. This technical article describes the technique of using a multiplanar instrumented cutting system and matching implants to perform TSA. The technique shown was previously studied and was found to allow surgeons to recreate the original anatomy of the humerus with very high precision.¹⁰

The humeral prosthesis described in this article has an articular surface that is slightly elliptical to more closely match the actual shape of the humerus bone.¹¹ Biomechanical studies have demonstrated that implants designed with a nonspherical shape have more similar motion and kinematics to those of the native humeral head.¹² The undersurface of the implant has a concave four-plane geometry that matches with the bone cuts created by the cutting guides (Figures 1, 2). 

This provides rotation stability, and the implant rests on the strong subchondral bone of the proximal humerus proximal to the anatomic neck rather than relying on metaphyseal bone or canal fixation, as recommended by Aldoiusti.¹³ It also allows optimal implant placement with complete freedom with respect to inclination, version, and medial/posterior offset from the humeral canal. 

Continue to: The implant respects the relationship...

The implant respects the relationship of the rotator cuff insertion and has a recessed superior margin to keep both the implant and the saw blade 3 mm to 5 mm away from the supraspinatus fibers to protect the rotator cuff from iatrogenic injury.

TECHNIQUE

The technique described in this article uses the Catalyst CSR Total Shoulder System (Catalyst OrthoScience), which was cleared to treat arthritis of the shoulder by the US Food and Drug Administration in May 2016.

A standard deltopectoral incision is made, and the surgeon dissects the interval between the pectoralis major medially and the deltoid laterally. The subscapularis can be incised by tenotomy; alternatively, the surgeon can perform a subscapularis peel or a lesser tuberosity osteotomy using this technique.

Once the glenohumeral joint is exposed, the surgeon delivers the humeral head anteriorly. A preferred method is to place a Darrach retractor between the humeral head and the glenoid, and a cobra or a second Darrach retractor behind the superolateral humeral head superficial to the supraspinatus tendon. By simultaneously pressing on both retractors and externally rotating the patient’s arm, the humeral head is delivered anteriorly. Osteophytes on the anterior and inferior edge of the humeral head are generously removed at this time using a rongeur.

Using a pin guide, the long 3.2-mm guidewire pin is drilled under power into the center of the articular surface. The pin guide is then removed, leaving the pin in the center of the humerus (Figure 3).

Continue to: Next, the surgeon...

Next, the surgeon slides the cannulated reamer over the long guidewire pin and under power removes a small portion of the humeral head subchondral bone until the surgeon feels and observes that the reamer is no longer removing bone (Figure 4). The patent-pending reamer design prevents the surgeon from removing more than a few millimeters of bone, after which point the reamer spins on the surface of the bone without resecting further.

The surgeon is aware that the reamer has achieved its desired depth when it is no longer creating new bone shavings, and the surgeon can hear and feel that the reamer is spinning and no longer cutting. Then the surgeon removes the reamer.

The surgeon places the first humeral cut guide over the long guidewire pin, oriented superiorly-inferiorly and secures the guide using 4 short pins, and the long pin is removed. The surgeon uses an oscillating saw to cut the anterior and posterior plane cuts through the saw captures in the cut guide (Figure 5). The humeral cut guide and short pins are removed (Figure 6).

The surgeon then applies the second humeral cut guide to the proximal humerus and secures it using 2 short pins. The surgeon then uses the 6-mm drill to drill the 4 holes for the pegs of the implant. The top portion of the guide is removed, and the surgeon makes the superior and inferior cuts along the top and bottom surfaces of the guide using an oscillating saw (Figure 7).

The surgeon then uses a rongeur to slightly round the edges of the 4 corners at the periphery of the humerus. The second humeral cut guide and short pins are removed (Figure 8).

Continue to: Next, the surgeon trials...

Next, the surgeon trials humeral implants to determine the correct implant size (Figure 9). Once the proper humeral size is chosen, the trial is removed and the humeral cover is placed over the prepared humeral head. The surgeon then proceeds to glenoid preparation (Figure 10), which is easily accessible and facilitated by angled planar cuts on the humeral head. Glenoid technique will be discussed in a subsequent article.

After glenoid preparation and insertion, the humerus is delivered anteriorly. The proximal humerus is washed and dried, and cement is applied to the peg holes in the humerus bone and the underside of the humeral implant. The implant is then inserted using the humeral impactor to apply pressure and assure that the implant is fully seated. Once the humeral cement is hardened, the glenohumeral joint is irrigated and closure begins. Postoperative radiograph is shown in Figure 11.

DISCUSSION

Numerous authors have demonstrated that accurate implant placement is crucial for restoring normal glenoid kinematics and motion,^1-4 while some authors have reported worsening clinical outcomes and higher rates of pain and implant loosening when the implants were not placed anatomically.^5-8 This is such an important concept that it essentially was the primary inspiration for creating this TSA system. In addition, the system utilizes a nonspherical, elliptical humeral head that more closely matches the anatomy of the proximal humerus,^14,15 and this type of shape has shown improved biomechanics in laboratory testing.¹²

Good results have been demonstrated in restoring the normal anatomy using stemmed devices on the radiographic analysis of cadavers.¹⁶ The creation of stemmed implants with variable inclination and offset has improved computer models¹⁷ compared with previous studies,¹⁸ with the exception of scenarios with extreme offset. 

In theory, resurfacing implants and implants without a canal stem should have a better implant placement than that with stemmed implants; however, the ability to restore the center of rotation was even worse for resurfacing prostheses, with 65% of all implants being measured as outliers postoperatively in one study.¹⁹ Most of the resurfacing implants and their instrumentation techniques offer little to help the surgeon control for implant height. The depth of the reaming is variable, not calibrated, and not correlated with the implant size, frequently leading to overstuffing after surgery. Second, the use of spherical prostheses forces the surgeon to choose between matching the superior-inferior humeral size, leading to overhang of the implant, or matching the anteroposterior, leading to frequent undersizing in the coronal plane. The nonspherical, elliptical head shape can potentially simplify implant selection.

In summary, new techniques have been developed in an attempt to achieve increased consistency and precision in TSA. By more accurately reproducing the proximal humeral anatomy, it is proposed that clinical outcomes in terms of the range of motion and patient satisfaction may also be improved through newer techniques. Cadaver studies have validated the anatomic precision of this technique.¹⁰ Clinical data comprising of patient-reported outcome measures and radiographic outcome studies are currently underway for this arthroplasty system.

ABSTRACT

Proper reconstruction of proximal humeral anatomy is of primary importance to maximize patient outcomes after total shoulder arthroplasty. This article describes a new arthroplasty technique, where a fixed multiplanar bone resection is made and a novel implant, which is designed to precisely match the bone resection, is inserted. 

Continue to: The success of total shoulder arthroplasty...

The success of total shoulder arthroplasty (TSA) is largely dependent on how accurate the proximal humeral anatomy is reconstructed and the glenohumeral relationships are restored.^1-4 Numerous studies have demonstrated a relationship of worse clinical outcomes and implant failure with nonanatomic implant placement.^5-8 The majority of arthroplasty systems rely on surgeon-dependent decision-making to determine the location of the border of the articular surface and, ultimately, the amount and location of bone to be resected. Even in experienced hands, the ability to reproducibly restore the joint line is inconsistent.³

In contrast, the majority of total knee arthroplasty (TKA) systems have been designed with instrumentation that guides the surgeon precisely regarding where and how much femoral bone must be resected, and the corresponding implant is designed with the same thickness to preserve the location of the joint line. Cutting block instrumentation rather than freehand cuts enables reproducibility of TKA while being performed for an estimated 700,000 times annually in the US.⁹

To achieve similar high levels of reproducibility in shoulder arthroplasty, a new technique was developed based on the principle of providing instrumentation to assist the surgeon in accurately restoring the proximal humeral joint line. This technical article describes the technique of using a multiplanar instrumented cutting system and matching implants to perform TSA. The technique shown was previously studied and was found to allow surgeons to recreate the original anatomy of the humerus with very high precision.¹⁰

The humeral prosthesis described in this article has an articular surface that is slightly elliptical to more closely match the actual shape of the humerus bone.¹¹ Biomechanical studies have demonstrated that implants designed with a nonspherical shape have more similar motion and kinematics to those of the native humeral head.¹² The undersurface of the implant has a concave four-plane geometry that matches with the bone cuts created by the cutting guides (Figures 1, 2). 

This provides rotation stability, and the implant rests on the strong subchondral bone of the proximal humerus proximal to the anatomic neck rather than relying on metaphyseal bone or canal fixation, as recommended by Aldoiusti.¹³ It also allows optimal implant placement with complete freedom with respect to inclination, version, and medial/posterior offset from the humeral canal. 

Continue to: The implant respects the relationship...

The implant respects the relationship of the rotator cuff insertion and has a recessed superior margin to keep both the implant and the saw blade 3 mm to 5 mm away from the supraspinatus fibers to protect the rotator cuff from iatrogenic injury.

TECHNIQUE

The technique described in this article uses the Catalyst CSR Total Shoulder System (Catalyst OrthoScience), which was cleared to treat arthritis of the shoulder by the US Food and Drug Administration in May 2016.

A standard deltopectoral incision is made, and the surgeon dissects the interval between the pectoralis major medially and the deltoid laterally. The subscapularis can be incised by tenotomy; alternatively, the surgeon can perform a subscapularis peel or a lesser tuberosity osteotomy using this technique.

Once the glenohumeral joint is exposed, the surgeon delivers the humeral head anteriorly. A preferred method is to place a Darrach retractor between the humeral head and the glenoid, and a cobra or a second Darrach retractor behind the superolateral humeral head superficial to the supraspinatus tendon. By simultaneously pressing on both retractors and externally rotating the patient’s arm, the humeral head is delivered anteriorly. Osteophytes on the anterior and inferior edge of the humeral head are generously removed at this time using a rongeur.

Using a pin guide, the long 3.2-mm guidewire pin is drilled under power into the center of the articular surface. The pin guide is then removed, leaving the pin in the center of the humerus (Figure 3).

Continue to: Next, the surgeon...

Next, the surgeon slides the cannulated reamer over the long guidewire pin and under power removes a small portion of the humeral head subchondral bone until the surgeon feels and observes that the reamer is no longer removing bone (Figure 4). The patent-pending reamer design prevents the surgeon from removing more than a few millimeters of bone, after which point the reamer spins on the surface of the bone without resecting further.

The surgeon is aware that the reamer has achieved its desired depth when it is no longer creating new bone shavings, and the surgeon can hear and feel that the reamer is spinning and no longer cutting. Then the surgeon removes the reamer.

The surgeon places the first humeral cut guide over the long guidewire pin, oriented superiorly-inferiorly and secures the guide using 4 short pins, and the long pin is removed. The surgeon uses an oscillating saw to cut the anterior and posterior plane cuts through the saw captures in the cut guide (Figure 5). The humeral cut guide and short pins are removed (Figure 6).

The surgeon then applies the second humeral cut guide to the proximal humerus and secures it using 2 short pins. The surgeon then uses the 6-mm drill to drill the 4 holes for the pegs of the implant. The top portion of the guide is removed, and the surgeon makes the superior and inferior cuts along the top and bottom surfaces of the guide using an oscillating saw (Figure 7).

The surgeon then uses a rongeur to slightly round the edges of the 4 corners at the periphery of the humerus. The second humeral cut guide and short pins are removed (Figure 8).

Continue to: Next, the surgeon trials...

Next, the surgeon trials humeral implants to determine the correct implant size (Figure 9). Once the proper humeral size is chosen, the trial is removed and the humeral cover is placed over the prepared humeral head. The surgeon then proceeds to glenoid preparation (Figure 10), which is easily accessible and facilitated by angled planar cuts on the humeral head. Glenoid technique will be discussed in a subsequent article.

After glenoid preparation and insertion, the humerus is delivered anteriorly. The proximal humerus is washed and dried, and cement is applied to the peg holes in the humerus bone and the underside of the humeral implant. The implant is then inserted using the humeral impactor to apply pressure and assure that the implant is fully seated. Once the humeral cement is hardened, the glenohumeral joint is irrigated and closure begins. Postoperative radiograph is shown in Figure 11.

DISCUSSION

Numerous authors have demonstrated that accurate implant placement is crucial for restoring normal glenoid kinematics and motion,^1-4 while some authors have reported worsening clinical outcomes and higher rates of pain and implant loosening when the implants were not placed anatomically.^5-8 This is such an important concept that it essentially was the primary inspiration for creating this TSA system. In addition, the system utilizes a nonspherical, elliptical humeral head that more closely matches the anatomy of the proximal humerus,^14,15 and this type of shape has shown improved biomechanics in laboratory testing.¹²

Good results have been demonstrated in restoring the normal anatomy using stemmed devices on the radiographic analysis of cadavers.¹⁶ The creation of stemmed implants with variable inclination and offset has improved computer models¹⁷ compared with previous studies,¹⁸ with the exception of scenarios with extreme offset. 

In theory, resurfacing implants and implants without a canal stem should have a better implant placement than that with stemmed implants; however, the ability to restore the center of rotation was even worse for resurfacing prostheses, with 65% of all implants being measured as outliers postoperatively in one study.¹⁹ Most of the resurfacing implants and their instrumentation techniques offer little to help the surgeon control for implant height. The depth of the reaming is variable, not calibrated, and not correlated with the implant size, frequently leading to overstuffing after surgery. Second, the use of spherical prostheses forces the surgeon to choose between matching the superior-inferior humeral size, leading to overhang of the implant, or matching the anteroposterior, leading to frequent undersizing in the coronal plane. The nonspherical, elliptical head shape can potentially simplify implant selection.

In summary, new techniques have been developed in an attempt to achieve increased consistency and precision in TSA. By more accurately reproducing the proximal humeral anatomy, it is proposed that clinical outcomes in terms of the range of motion and patient satisfaction may also be improved through newer techniques. Cadaver studies have validated the anatomic precision of this technique.¹⁰ Clinical data comprising of patient-reported outcome measures and radiographic outcome studies are currently underway for this arthroplasty system.

ABSTRACT

Proper reconstruction of proximal humeral anatomy is of primary importance to maximize patient outcomes after total shoulder arthroplasty. This article describes a new arthroplasty technique, where a fixed multiplanar bone resection is made and a novel implant, which is designed to precisely match the bone resection, is inserted. 

Continue to: The success of total shoulder arthroplasty...

The success of total shoulder arthroplasty (TSA) is largely dependent on how accurate the proximal humeral anatomy is reconstructed and the glenohumeral relationships are restored.^1-4 Numerous studies have demonstrated a relationship of worse clinical outcomes and implant failure with nonanatomic implant placement.^5-8 The majority of arthroplasty systems rely on surgeon-dependent decision-making to determine the location of the border of the articular surface and, ultimately, the amount and location of bone to be resected. Even in experienced hands, the ability to reproducibly restore the joint line is inconsistent.³

In contrast, the majority of total knee arthroplasty (TKA) systems have been designed with instrumentation that guides the surgeon precisely regarding where and how much femoral bone must be resected, and the corresponding implant is designed with the same thickness to preserve the location of the joint line. Cutting block instrumentation rather than freehand cuts enables reproducibility of TKA while being performed for an estimated 700,000 times annually in the US.⁹

To achieve similar high levels of reproducibility in shoulder arthroplasty, a new technique was developed based on the principle of providing instrumentation to assist the surgeon in accurately restoring the proximal humeral joint line. This technical article describes the technique of using a multiplanar instrumented cutting system and matching implants to perform TSA. The technique shown was previously studied and was found to allow surgeons to recreate the original anatomy of the humerus with very high precision.¹⁰

The humeral prosthesis described in this article has an articular surface that is slightly elliptical to more closely match the actual shape of the humerus bone.¹¹ Biomechanical studies have demonstrated that implants designed with a nonspherical shape have more similar motion and kinematics to those of the native humeral head.¹² The undersurface of the implant has a concave four-plane geometry that matches with the bone cuts created by the cutting guides (Figures 1, 2). 

This provides rotation stability, and the implant rests on the strong subchondral bone of the proximal humerus proximal to the anatomic neck rather than relying on metaphyseal bone or canal fixation, as recommended by Aldoiusti.¹³ It also allows optimal implant placement with complete freedom with respect to inclination, version, and medial/posterior offset from the humeral canal. 

Continue to: The implant respects the relationship...

The implant respects the relationship of the rotator cuff insertion and has a recessed superior margin to keep both the implant and the saw blade 3 mm to 5 mm away from the supraspinatus fibers to protect the rotator cuff from iatrogenic injury.

TECHNIQUE

The technique described in this article uses the Catalyst CSR Total Shoulder System (Catalyst OrthoScience), which was cleared to treat arthritis of the shoulder by the US Food and Drug Administration in May 2016.

A standard deltopectoral incision is made, and the surgeon dissects the interval between the pectoralis major medially and the deltoid laterally. The subscapularis can be incised by tenotomy; alternatively, the surgeon can perform a subscapularis peel or a lesser tuberosity osteotomy using this technique.

Once the glenohumeral joint is exposed, the surgeon delivers the humeral head anteriorly. A preferred method is to place a Darrach retractor between the humeral head and the glenoid, and a cobra or a second Darrach retractor behind the superolateral humeral head superficial to the supraspinatus tendon. By simultaneously pressing on both retractors and externally rotating the patient’s arm, the humeral head is delivered anteriorly. Osteophytes on the anterior and inferior edge of the humeral head are generously removed at this time using a rongeur.

Using a pin guide, the long 3.2-mm guidewire pin is drilled under power into the center of the articular surface. The pin guide is then removed, leaving the pin in the center of the humerus (Figure 3).

Continue to: Next, the surgeon...

Next, the surgeon slides the cannulated reamer over the long guidewire pin and under power removes a small portion of the humeral head subchondral bone until the surgeon feels and observes that the reamer is no longer removing bone (Figure 4). The patent-pending reamer design prevents the surgeon from removing more than a few millimeters of bone, after which point the reamer spins on the surface of the bone without resecting further.

The surgeon is aware that the reamer has achieved its desired depth when it is no longer creating new bone shavings, and the surgeon can hear and feel that the reamer is spinning and no longer cutting. Then the surgeon removes the reamer.

The surgeon places the first humeral cut guide over the long guidewire pin, oriented superiorly-inferiorly and secures the guide using 4 short pins, and the long pin is removed. The surgeon uses an oscillating saw to cut the anterior and posterior plane cuts through the saw captures in the cut guide (Figure 5). The humeral cut guide and short pins are removed (Figure 6).

The surgeon then applies the second humeral cut guide to the proximal humerus and secures it using 2 short pins. The surgeon then uses the 6-mm drill to drill the 4 holes for the pegs of the implant. The top portion of the guide is removed, and the surgeon makes the superior and inferior cuts along the top and bottom surfaces of the guide using an oscillating saw (Figure 7).

The surgeon then uses a rongeur to slightly round the edges of the 4 corners at the periphery of the humerus. The second humeral cut guide and short pins are removed (Figure 8).

Continue to: Next, the surgeon trials...

Next, the surgeon trials humeral implants to determine the correct implant size (Figure 9). Once the proper humeral size is chosen, the trial is removed and the humeral cover is placed over the prepared humeral head. The surgeon then proceeds to glenoid preparation (Figure 10), which is easily accessible and facilitated by angled planar cuts on the humeral head. Glenoid technique will be discussed in a subsequent article.

After glenoid preparation and insertion, the humerus is delivered anteriorly. The proximal humerus is washed and dried, and cement is applied to the peg holes in the humerus bone and the underside of the humeral implant. The implant is then inserted using the humeral impactor to apply pressure and assure that the implant is fully seated. Once the humeral cement is hardened, the glenohumeral joint is irrigated and closure begins. Postoperative radiograph is shown in Figure 11.

DISCUSSION

Numerous authors have demonstrated that accurate implant placement is crucial for restoring normal glenoid kinematics and motion,^1-4 while some authors have reported worsening clinical outcomes and higher rates of pain and implant loosening when the implants were not placed anatomically.^5-8 This is such an important concept that it essentially was the primary inspiration for creating this TSA system. In addition, the system utilizes a nonspherical, elliptical humeral head that more closely matches the anatomy of the proximal humerus,^14,15 and this type of shape has shown improved biomechanics in laboratory testing.¹²

Good results have been demonstrated in restoring the normal anatomy using stemmed devices on the radiographic analysis of cadavers.¹⁶ The creation of stemmed implants with variable inclination and offset has improved computer models¹⁷ compared with previous studies,¹⁸ with the exception of scenarios with extreme offset. 

In theory, resurfacing implants and implants without a canal stem should have a better implant placement than that with stemmed implants; however, the ability to restore the center of rotation was even worse for resurfacing prostheses, with 65% of all implants being measured as outliers postoperatively in one study.¹⁹ Most of the resurfacing implants and their instrumentation techniques offer little to help the surgeon control for implant height. The depth of the reaming is variable, not calibrated, and not correlated with the implant size, frequently leading to overstuffing after surgery. Second, the use of spherical prostheses forces the surgeon to choose between matching the superior-inferior humeral size, leading to overhang of the implant, or matching the anteroposterior, leading to frequent undersizing in the coronal plane. The nonspherical, elliptical head shape can potentially simplify implant selection.

In summary, new techniques have been developed in an attempt to achieve increased consistency and precision in TSA. By more accurately reproducing the proximal humeral anatomy, it is proposed that clinical outcomes in terms of the range of motion and patient satisfaction may also be improved through newer techniques. Cadaver studies have validated the anatomic precision of this technique.¹⁰ Clinical data comprising of patient-reported outcome measures and radiographic outcome studies are currently underway for this arthroplasty system.

As a professional society supporting an increasingly diverse membership base, SHM is perpetually challenged to create an environment that offers relevance and community to all. While the broad hospital medicine population and SHM are focused on the same goals – optimizing patient care and the system that delivers it – there are nuances within membership that require specific networks and platforms to build this environment of community.

SHM relies on both staff and volunteers to be an engine for leadership, innovation, and labor. Its volunteer corps is essential to delivering value to members and setting the strategic agenda for hospital medicine’s future. Over the last year, SHM has attempted to expand the infrastructure and opportunity for volunteer leadership by examining new approaches to allow pockets of membership to have their own voice. During the year to come, members will continue to help staff forge a new landscape for constituency engagement.

If you are a current volunteer leader, were interested in pursuing volunteer opportunities this past fall, or have simply been navigating SHM’s new website, you may be aware that the Committee structure has changed. There is also new publicity for things called “Special Interest Groups.” Many of our constituency-based Committees are in the process of transforming into Special Interest Groups, which will be officially launched during SHM’s Annual Conference in Orlando in April. They are adopting a more visible charge to create the most accessible and influence-able environment for the entire SHM community.

Committee-to-Special Interest Group transition is about both philosophy and mechanics. It aims to ensure that each constituency group can be readily shaped by the entire population it represents, and will work to create the infrastructure to facilitate that. SHM envisions Special Interest Groups being primary influencers over future content-development and policy objectives; their online communities serving as the principal means for socialization and dialogue around proposed ideas and initiatives. To that end, SHM invested in an entirely new platform for Hospital Medicine Exchange (HMX). If you have yet to explore the new HMX and opportunities for niche networking, visit www.hmxchange.org.

We have also developed a new governance model to encourage interactions between volunteer groups. While there is overlap within Committee and Special Interest Group constructs and likely many volunteers serving in both spheres, it is important to create parallel environments with discreet charges around function and membership engagement. As we continue to roll out the changes, we will rely on volunteers and members at large to help us best realize our intent.

During this transformation, existing volunteers are working with staff to determine the future. There will be some differences in the way Committees and Special Interest Groups function. The differences will be deliberate and designed to provide for fluid and thoughtful administration of business and membership engagement. There will also be consistent communication between Special Interest Groups and strategic and functional Committees with ongoing charges and oversight of existing SHM programs.

Special Interest Groups will have dedicated staff liaisons and volunteer leadership councils. Transforming Committees’ current volunteers will serve as inaugural council leaders with the process for future election being developed in concert by staff and volunteers over the next several months. Special Interest Group membership is open and free to all active SHM members. All current Special Interest Groups will facilitate live Special Interest Forums during SHM’s Annual Conference. If you attend Hospital Medicine 2018 in April, stop by these Special Interest Group Forums on the following topics to learn more:

• Advocacy and Public Policy

• Care for Vulnerable Populations

• Critical Care Medicine

• Health care Information Technology

• Hospitalists Trained in Family Medicine

• Med-Peds Hospitalists

• Multi-Site Hospital Leaders

• Nurse Practitioners and Physician Assistants

• Palliative Care

• Pediatric Hospitalists

• Perioperative Medicine

• Point of Care Ultrasound

• Practice Administrators/Practice Management

• Quality Improvement

• Medical Students and Residents

• Rural Hospitalists

Summaries of the live forums will be posted on corresponding HMX communities after the conference, complete with open comment periods for discussion. There will be an open application period during summer 2018 for new Special Interest Groups not defined above. The SHM Board will review applications in September 2018, and newly established groups will be convened in October to begin building HMX communities, confirming volunteer leader councils, and charting their course with a dedicated staff liaison.

The intent is simple – to provide an open-access mechanism for membership at large to collaborate amongst themselves, offer perspective, and validate or challenge SHM’s proposed initiatives and direction. Special Interest Groups and Committees alike are – and will be – an essential part of SHM’s future – as developers of content, voices of the populations we serve, and an apparatus for the implementation of our shared mission and vision.

SHM exists to serve its members and help them deliver exceptional patient care. We are always interested in your perspective and feedback. To offer your thoughts and ideas about Special Interest Groups or anything else related to membership, please email [email protected].
 

Mr. Gray is vice president of membership at the Society of Hospital Medicine.

As a professional society supporting an increasingly diverse membership base, SHM is perpetually challenged to create an environment that offers relevance and community to all. While the broad hospital medicine population and SHM are focused on the same goals – optimizing patient care and the system that delivers it – there are nuances within membership that require specific networks and platforms to build this environment of community.

SHM relies on both staff and volunteers to be an engine for leadership, innovation, and labor. Its volunteer corps is essential to delivering value to members and setting the strategic agenda for hospital medicine’s future. Over the last year, SHM has attempted to expand the infrastructure and opportunity for volunteer leadership by examining new approaches to allow pockets of membership to have their own voice. During the year to come, members will continue to help staff forge a new landscape for constituency engagement.

If you are a current volunteer leader, were interested in pursuing volunteer opportunities this past fall, or have simply been navigating SHM’s new website, you may be aware that the Committee structure has changed. There is also new publicity for things called “Special Interest Groups.” Many of our constituency-based Committees are in the process of transforming into Special Interest Groups, which will be officially launched during SHM’s Annual Conference in Orlando in April. They are adopting a more visible charge to create the most accessible and influence-able environment for the entire SHM community.

Committee-to-Special Interest Group transition is about both philosophy and mechanics. It aims to ensure that each constituency group can be readily shaped by the entire population it represents, and will work to create the infrastructure to facilitate that. SHM envisions Special Interest Groups being primary influencers over future content-development and policy objectives; their online communities serving as the principal means for socialization and dialogue around proposed ideas and initiatives. To that end, SHM invested in an entirely new platform for Hospital Medicine Exchange (HMX). If you have yet to explore the new HMX and opportunities for niche networking, visit www.hmxchange.org.

We have also developed a new governance model to encourage interactions between volunteer groups. While there is overlap within Committee and Special Interest Group constructs and likely many volunteers serving in both spheres, it is important to create parallel environments with discreet charges around function and membership engagement. As we continue to roll out the changes, we will rely on volunteers and members at large to help us best realize our intent.

During this transformation, existing volunteers are working with staff to determine the future. There will be some differences in the way Committees and Special Interest Groups function. The differences will be deliberate and designed to provide for fluid and thoughtful administration of business and membership engagement. There will also be consistent communication between Special Interest Groups and strategic and functional Committees with ongoing charges and oversight of existing SHM programs.

Special Interest Groups will have dedicated staff liaisons and volunteer leadership councils. Transforming Committees’ current volunteers will serve as inaugural council leaders with the process for future election being developed in concert by staff and volunteers over the next several months. Special Interest Group membership is open and free to all active SHM members. All current Special Interest Groups will facilitate live Special Interest Forums during SHM’s Annual Conference. If you attend Hospital Medicine 2018 in April, stop by these Special Interest Group Forums on the following topics to learn more:

• Advocacy and Public Policy

• Care for Vulnerable Populations

• Critical Care Medicine

• Health care Information Technology

• Hospitalists Trained in Family Medicine

• Med-Peds Hospitalists

• Multi-Site Hospital Leaders

• Nurse Practitioners and Physician Assistants

• Palliative Care

• Pediatric Hospitalists

• Perioperative Medicine

• Point of Care Ultrasound

• Practice Administrators/Practice Management

• Quality Improvement

• Medical Students and Residents

• Rural Hospitalists

Summaries of the live forums will be posted on corresponding HMX communities after the conference, complete with open comment periods for discussion. There will be an open application period during summer 2018 for new Special Interest Groups not defined above. The SHM Board will review applications in September 2018, and newly established groups will be convened in October to begin building HMX communities, confirming volunteer leader councils, and charting their course with a dedicated staff liaison.

The intent is simple – to provide an open-access mechanism for membership at large to collaborate amongst themselves, offer perspective, and validate or challenge SHM’s proposed initiatives and direction. Special Interest Groups and Committees alike are – and will be – an essential part of SHM’s future – as developers of content, voices of the populations we serve, and an apparatus for the implementation of our shared mission and vision.

SHM exists to serve its members and help them deliver exceptional patient care. We are always interested in your perspective and feedback. To offer your thoughts and ideas about Special Interest Groups or anything else related to membership, please email [email protected].
 

Mr. Gray is vice president of membership at the Society of Hospital Medicine.

As a professional society supporting an increasingly diverse membership base, SHM is perpetually challenged to create an environment that offers relevance and community to all. While the broad hospital medicine population and SHM are focused on the same goals – optimizing patient care and the system that delivers it – there are nuances within membership that require specific networks and platforms to build this environment of community.

SHM relies on both staff and volunteers to be an engine for leadership, innovation, and labor. Its volunteer corps is essential to delivering value to members and setting the strategic agenda for hospital medicine’s future. Over the last year, SHM has attempted to expand the infrastructure and opportunity for volunteer leadership by examining new approaches to allow pockets of membership to have their own voice. During the year to come, members will continue to help staff forge a new landscape for constituency engagement.

If you are a current volunteer leader, were interested in pursuing volunteer opportunities this past fall, or have simply been navigating SHM’s new website, you may be aware that the Committee structure has changed. There is also new publicity for things called “Special Interest Groups.” Many of our constituency-based Committees are in the process of transforming into Special Interest Groups, which will be officially launched during SHM’s Annual Conference in Orlando in April. They are adopting a more visible charge to create the most accessible and influence-able environment for the entire SHM community.

Committee-to-Special Interest Group transition is about both philosophy and mechanics. It aims to ensure that each constituency group can be readily shaped by the entire population it represents, and will work to create the infrastructure to facilitate that. SHM envisions Special Interest Groups being primary influencers over future content-development and policy objectives; their online communities serving as the principal means for socialization and dialogue around proposed ideas and initiatives. To that end, SHM invested in an entirely new platform for Hospital Medicine Exchange (HMX). If you have yet to explore the new HMX and opportunities for niche networking, visit www.hmxchange.org.

We have also developed a new governance model to encourage interactions between volunteer groups. While there is overlap within Committee and Special Interest Group constructs and likely many volunteers serving in both spheres, it is important to create parallel environments with discreet charges around function and membership engagement. As we continue to roll out the changes, we will rely on volunteers and members at large to help us best realize our intent.

During this transformation, existing volunteers are working with staff to determine the future. There will be some differences in the way Committees and Special Interest Groups function. The differences will be deliberate and designed to provide for fluid and thoughtful administration of business and membership engagement. There will also be consistent communication between Special Interest Groups and strategic and functional Committees with ongoing charges and oversight of existing SHM programs.

Special Interest Groups will have dedicated staff liaisons and volunteer leadership councils. Transforming Committees’ current volunteers will serve as inaugural council leaders with the process for future election being developed in concert by staff and volunteers over the next several months. Special Interest Group membership is open and free to all active SHM members. All current Special Interest Groups will facilitate live Special Interest Forums during SHM’s Annual Conference. If you attend Hospital Medicine 2018 in April, stop by these Special Interest Group Forums on the following topics to learn more:

• Advocacy and Public Policy

• Care for Vulnerable Populations

• Critical Care Medicine

• Health care Information Technology

• Hospitalists Trained in Family Medicine

• Med-Peds Hospitalists

• Multi-Site Hospital Leaders

• Nurse Practitioners and Physician Assistants

• Palliative Care

• Pediatric Hospitalists

• Perioperative Medicine

• Point of Care Ultrasound

• Practice Administrators/Practice Management

• Quality Improvement

• Medical Students and Residents

• Rural Hospitalists

Summaries of the live forums will be posted on corresponding HMX communities after the conference, complete with open comment periods for discussion. There will be an open application period during summer 2018 for new Special Interest Groups not defined above. The SHM Board will review applications in September 2018, and newly established groups will be convened in October to begin building HMX communities, confirming volunteer leader councils, and charting their course with a dedicated staff liaison.

The intent is simple – to provide an open-access mechanism for membership at large to collaborate amongst themselves, offer perspective, and validate or challenge SHM’s proposed initiatives and direction. Special Interest Groups and Committees alike are – and will be – an essential part of SHM’s future – as developers of content, voices of the populations we serve, and an apparatus for the implementation of our shared mission and vision.

SHM exists to serve its members and help them deliver exceptional patient care. We are always interested in your perspective and feedback. To offer your thoughts and ideas about Special Interest Groups or anything else related to membership, please email [email protected].
 

Mr. Gray is vice president of membership at the Society of Hospital Medicine.

Deep venous thrombosis and pulmonary embolism, collectively known as venous thromboembolism (VTE), affect up to 600,000 Americans a year.¹ Most of these are hospital-associated venous thromboembolisms (HA-VTE).^1,2 VTE poses a substantial risk of mortality and long-term morbidity, and its treatment poses a risk of major bleeding.¹ As appropriate VTE prophylaxis (“prophylaxis”) can reduce the risk of VTE by 40% to 80% depending on the patient population,³ VTE risk assessment and prophylaxis is endorsed by multiple guidelines^4-7 and supported by regulatory agencies.^8-10

However, despite extensive study, consensus about the impact of prophylaxis^4,11 and the optimal method of risk assessment^4,5,7,12 is lacking. Meanwhile, implementation of prophylaxis in real-world settings is poor; only 40% to 60% of at-risk patients receive prophylaxis,¹³ and as few as <20% receive optimal prophylaxis.¹⁴ Both systematic reviews^15,16 and experience with VTE prevention collaboratives^17,18 found that multifaceted interventions and alerts may be most effective in improving prophylaxis rates, but without proof of improved VTE rates.¹⁵ There is limited experience with large-scale VTE prevention. Organizations like The Joint Commission (TJC)⁸ and the Surgical Care Improvement Project have promoted quality measures but without clear evidence of improvement.¹⁹ In addition, an analysis of over 20,000 medical patients at 35 hospitals found no difference in VTE rates between high- and low-performing hospitals,²⁰ suggesting that aggressive prophylaxis efforts may not reduce VTE, at least among medical patients.²¹ However, a 5-hospital University of California collaborative was associated with improved VTE rates, chiefly among surgical patients.²²

In 2011, Dignity Health targeted VTE for improvement after investigations of potentially preventable HA-VTE revealed variable patterns of prophylaxis. In addition, improvement seemed feasible because there is a proven framework for VTE quality improvement (QI) projects^17,18 and a record of success with the following 3 specific strategies: quality mentorship,²³ use of a simple VTE risk assessment method, and active surveillance (real-time monitoring targeting suboptimal prophylaxis with concurrent intervention). This active surveillance technique has been used successfully in prior improvement efforts, often termed measure-vention.^17,18,22,24

Setting and Participants

The QI collaborative was performed at 35 Dignity Health community hospitals in California, Arizona, and Nevada. Facilities ranged from 25 to 571 beds in size with a mixture of teaching and nonteaching hospitals. Prior to the initiative, prophylaxis improvement efforts were incomplete and inconsistent at study facilities. All adult acute care inpatients at all facilities were included except rehabilitation, behavioral health, skilled nursing, hospice, other nonacute care, and inpatient deliveries.

Design Overview

We performed a prospective, unblinded, open-intervention study of a QI collaborative in 35 community hospitals and studied the effect on prophylaxis and VTE rates with historical controls. The 35 hospitals were organized into 2 cohorts. In the “pilot” cohort, 9 hospitals (chosen to be representative of the various settings, size, and teaching status within the Dignity system) received funding from the Gordon and Betty Moore Foundation (GBMF) for intensive, individualized QI mentorship from experts as well as active surveillance (see “Interventions”). The pilot sites led the development of the VTE risk assessment and prophylaxis protocol (“VTE protocol”), measures, order sets, implementation tactics, and lessons learned, assisted by the mentor experts. Dissemination to the 26-hospital “spread” cohort was facilitated by the Dignity Health Hospital Engagement Network (HEN) infrastructure.

Timeline

Two of the pilot sites, acting as leads on the development of protocol and order set tools, formed improvement teams in March 2011, 6 to 12 months earlier than other Dignity sites. Planning and design work occurred from March 2011 to September 2012. Most implementation at the 35 hospitals occurred in a staggered fashion during calendar year (CY) 2012 and 2013 (see Figure 1). As few changes were made until mid-2012, we considered CY 2011 the baseline for comparison, CY 2012 to 2013 the implementation years, and CY 2014 the postimplementation period.

The project was reviewed by the Institutional Review Board (IRB) of Dignity Health and determined to be an IRB-exempt QI project.

Interventions

Collaborative Infrastructure

Multidisciplinary Teams

Improvement teams formed between March 2011 and September 2012. Members included a physician champion, frontline nurses and physicians, an administrative liaison, pharmacists, quality and data specialists, clinical informatics staff, and stakeholders from key clinical services. Teams met at least monthly at each site.

Physician Mentors

The 9 pilot sites received individualized mentorship provided by outside experts (IJ or GM) based on a model pioneered by the Society of Hospital Medicine’s (SHM) Mentored Implementation programs.²³ Each pilot site completed a self-assessment survey¹⁷ (see supplementary Appendix A) about past efforts, team composition, current performance, aims, barriers, and opportunities. The mentors reviewed the completed questionnaire with each hospital and provided advice on the VTE protocol and order set design, measurement, and benchmarking during 3 webinar meetings scheduled at 0, 3, and 9 months, plus as-needed e-mail and phone correspondence. After each webinar, the mentors provided detailed improvement suggestions (see supplementary Appendix B). Several hospitals received mentor site visits, which focused on unit rounding, active surveillance, staff and provider education, and problem-solving sessions with senior leadership, physician leadership, and the improvement team.

VTE Protocol

After a literature review and consultation with the mentors, Dignity Health developed and implemented a VTE protocol, modified from a model used in previous improvement efforts.^18,22-24 Its risk assessment method is often referred to as a “3 bucket” model because it assigns patients to high-, moderate-, or low-risk categories based on clinical factors (eg, major orthopedic surgery, prior VTE, and others), and the VTE protocol recommends interventions based on the risk category (see supplementary Appendix C). Dignity Health was transitioning to a single electronic health record (Cerner Corporation, North Kansas City, MO) during the study, and study hospitals were using multiple platforms, necessitating the development of both paper and electronic versions of the VTE protocol. The electronic version required completion of the VTE protocol for all inpatient admissions and transfers. The VTE protocol was completed in November 2011 and disseminated to other sites in a staggered fashion through November 2012. Completed protocols and improvement tips were shared by the project lead and by webinar sessions. Sites were also encouraged to implement a standardized practice that allowed nurses to apply sequential compression devices to at-risk patients without physician orders when indicated by protocol, when contraindications such as vascular disease or ulceration were absent.

Education

Staff were educated about the VTE protocol by local teams, starting between late 2011 and September 2012. The audience (physicians, nurses, pharmacists, etc.) and methods (conferences, fliers, etc.) were determined by local teams, following guidance by mentors and webinar content. Active surveillance provided opportunities for in-the-moment, patient-specific education and protocol reinforcement. Both mentors delivered educational presentations at pilot sites.

Active Surveillance

Sites were encouraged to perform daily review of prophylaxis adequacy for inpatients and correct lapses in real time (both under- and overprophylaxis). Inappropriate prophylaxis orders were addressed by contacting providers to change the order or document the rationale not to. Lapses in adherence to prophylaxis were addressed by nursing correction and education of involved staff. Active surveillance was funded for 10 hours a week at pilot sites. Spread sites received only minimal support from HEN monies. All sites used daily prophylaxis reports, enhanced to include contraindications like thrombocytopenia and coagulopathy, to facilitate efforts. Active surveillance began in May 2012 in the lead pilot hospitals and was implemented in other sites between October 2012 and February 2013.

Metrics

Prophylaxis Rates

Measurement of prophylaxis did not begin until 2012 to 2013; thus, the true baseline rate for prophylaxis was not captured. TJC metrics (VTE-1 and VTE-2)²⁵ were consolidated into a composite TJC prophylaxis rate from January 2012 to December 2014 for both pilot and spread hospitals. These measures assess the percentage of adult inpatients who received VTE prophylaxis or have documentation of why no prophylaxis was given the day of or day after hospital admission (VTE-1) or the day of or day after ICU admission or transfer (VTE-2). These measures are met if any mechanical or pharmacologic prophylaxis was delivered.

In addition to the TJC metric, the 9 pilot hospitals monitored rates of protocol-compliant prophylaxis for 12 to 20 months. Each patient’s prophylaxis was considered protocol compliant if it was consistent with the prophylaxis protocol at the time of the audit or if contraindications were documented (eg, patients eligible for, but with contraindications to, pharmacologic prophylaxis had to have an order for mechanical prophylaxis or documented contraindication to both modalities). As this measure was initiated in a staggered fashion, the rate of protocol-compliant prophylaxis is summarized for consecutive months of measurement rather than consecutive calendar months.

HA-VTE Rates

VTE events were captured by review of electronic coding data for the International Classification of Diseases, 9th Revision (ICD-9) codes 415.11-415.19, 453.2, 453.40-453.42, and 453.8-453.89. HA-VTE was defined as either new VTE not present on admission (NPOA HA-VTE) or new VTE presenting in a readmitted patient within 30 days of discharge (Readmit HA-VTE). Cases were stratified based on whether the patient had undergone a major operation (surgery patients) or not (medical patients) as identified by Medicare Services diagnosis-related group codes.

Control Measures

Potential adverse events were captured by review of electronic coding data for ICD-9 codes 289.84 (heparin-induced thrombocytopenia [HIT]) and E934.2 (adverse effects because of anticoagulants).

Statistical Analysis

Statistical process control charts were used to depict changes in prophylaxis rates over the 3 years for which data was collected. For VTE and safety outcomes, Pearson χ² value with relative risk (RR) calculations and 95% confidence intervals (CIs) were used to compare proportions between groups at baseline (CY 2011) versus postimplementation (CY 2014). Differences between the means of normally distributed data were calculated, and a 95% CI for the difference between the means was performed to assess statistical difference. Nonparametric characteristics were described by quartiles and interquartile range, and the 2-sided Mann-Whitney U test was performed to assess statistical difference between the CY 2011 and CY 2014 period.

Role of the Funding Source

The GBMF funded the collaborative and supported authorship of the manuscript but had no role in the design or conduct of the intervention, the collection or analysis of data, or the drafting of the manuscript.

Population Demographics

There were 1,155,069 adult inpatient admissions during the 4-year study period (264,280 in the 9 pilot sites, 890,789 in the 26 spread sites). There were no clinically relevant changes in gender distribution, mortality rate, median age, case mix index, or hospital length of stay in 2011 versus 2014. Men comprised 47.1% of the patient population in 2011 and 47.7% in 2014. The mortality rate was 2.7% in both years. Median age was 62 in 2011 and 63 in 2014. The mean case mix index (1.58 vs 1.65) and mean length of stay (4.29 vs 4.33 days) were similar in the 2 time periods.

Prophylaxis Rates

TJC Prophylaxis rates

Rates of Protocol-Compliant Prophylaxis

There were 34,071 active surveillance audits across the 20 months of reporting in the pilot cohort (mean, 1817 audits per month). The rate of protocol-compliant prophylaxis improved from 89% at month 1 of observation to 93% during month 2 and 97% by the last 3 months (Pearson χ² P < .001 for both comparisons).

HA-VTE

HA-VTE characteristics

Improved HA-VTE over Time

Four hundred twenty-eight fewer HA-VTEs occurred in 2014 than in 2011 (RR 0.78; 95% CI, 0.73-0.85) (Table and Figure 3). Readmission HA-VTEs were reduced by 315 (RR 0.72; 95% CI, 0.65-0.80), while the reduction in NPOA HA-VTEs was less robust (RR 0.88; 95% CI, 0.79-0.99). Pilot sites enjoyed a more robust reduction in HA-VTEs than spread sites (26% vs 20%), largely because the pilot cohort enjoyed a 34% reduction in NPOA HA-VTEs and a 20% reduction in Readmit HA-VTEs, while the spread cohort only achieved reductions in Readmit HA-VTEs.

Safety

Rates of HIT and adverse effects because of anticoagulants were low (Table). The rate of HIT declined from 178 events in 2011 to 109 in 2014 (RR 0.66; 95% CI, 0.52-0.84), and the RR of anticoagulant adverse events remained stable (RR 1.01; 95% CI, 0.87-1.15).

Our QI project, based on a proven collaborative approach and mentorship,^18,22,24 order set redesign, and active surveillance, was associated with 26% less VTEs in the pilot cohort and 20% less VTEs in the spread cohort. These gains, down to a final rate of approximately 4 HA-VTEs per 1000 admissions, occurred despite a low baseline HA-VTE rate. Dignity Health achieved these improvements in 35 hospitals with varied sizes, settings, ordering systems, and teaching statuses, achieving what is to our knowledge the largest VTE QI initiative yet reported.

Implementation experiences were not systematically recorded, and techniques were not compared with a control group. However, we believe that Dignity Health’s organizational commitment to improvement and centralized support were crucial for success. In addition, the pilot sites received grant support from the GBMF for intensive quality mentoring, a strategy with demonstrated value.²³ Mentors and team members noted that system-wide revision to the computerized physician order entry system was easiest to implement, while active surveillance represented the most labor-intensive intervention. Other experiences echoed lessons from previous VTE mentorship efforts.^17,18

The selection of a VTE protocol conducive to implementation and provider use was a key strategy. The ideal approach to VTE risk assessment is not known,^12,26 but guidelines either offer no specific guidance⁷ or would require implementation of 3 different systems per hospital.^4,5 Several of these are point scoring systems, which may have lower clinician acceptance or require programming to improve real-world use^18,26,27; the Padua score was derived from a patient population that differs significantly from those in the United States.¹² Our study provides more practical experience with a “3-bucket” model, which has previously shown high interobserver reliability, good clinician acceptance, and meaningful reductions of VTE, including in American patient populations.^18,22,24

The value of VTE prophylaxis is still disputed in many inpatient groups. The overall rate of HA-VTE is low, so the per-patient benefit of prophylaxis is low, and many patients may be overprophylaxed.^4,11,12 Recently, Flanders et al.²⁰ reported that HA-VTE rates among 20,800 medical inpatients in Michigan were low (about 1%) and similar at hospitals in the top (mean prophylaxis rate 86%) or bottom (mean prophylaxis rate 56%) tertiles of performance. Possible explanations for the differences between their multicenter experience and ours include our sample size (55 times larger) and the possibility that targeting prophylaxis to patients at highest need (captured in our protocol-compliant prophylaxis rates) matters more than prophylaxing a percent of the population.

Further research is needed to develop simple, easy-to-implement methods to identify inpatients who do not, or no longer, require prophylaxis.¹² Hospital systems also need methods to determine if prophylaxis improvement efforts can lower their HA-VTE rates and in which subpopulations. For example, a collaborative effort at the University of California lowered HA-VTE rates toward a common improved rate of 0.65% to 0.73%,²² while Dignity Health achieved improvement despite starting with an even lower baseline. In the University of California collaborative, benefits were limited chiefly to surgical patients, while Dignity Health achieved most improvement in medical patients, particularly in Readmit HA-VTE. If future research uncovers the reasons for these differences, it could help hospitals decide where to target improvement efforts.

Our study has several limitations. First, we used a nonrandomized time series design, so we cannot exclude other potential explanations for the change in VTE rates. However, there were no major changes in patient populations or concurrent projects likely to have influenced event rates. While we did not collect detailed demographic information on subjects, the broad inclusion criteria and multicenter design suggests a high degree of generalizability. Second, we followed inpatient VTE events and VTE-related readmissions, but not VTE treated in the outpatient setting. This did not change over the study, but the availability of all-oral therapy for VTE could have caused underdetection if clinic or emergency room doctors sent home more patients on oral therapy instead of readmitting them to the hospital. Third, implementation was enhanced by GBMF funds (at 9 sites, with the remainder benefitting from their experience), a shared electronic medical record at many sites, and a strong organizational safety culture, which may limit generalizability. However, spread sites showed similar improvement, paper-based sites were included, and the mentorship and quality collaborative models are scalable at low cost. Fourth, some QI efforts began at some pilot sites in CY 2011, so we could not compare completely clean pre- and postproject timeframes. However, early improvement would have resulted in an underestimation of the project’s impact. Lastly, the reason for a decline in HIT rates is not known. Standardized order sets promoted preferential use of low molecular weight heparin, which is less likely to induce HIT, and active surveillance targeted overprophylaxis as well as underprophylaxis, but we do not have data on heparin utilization patterns to confirm or refute these possibilities.

Strengths of our study include reductions in HA-VTE, both with and without access to GBMF funds, by using broadly available QI strategies.¹⁷ This real-world success and ease of dissemination are particularly important because the clinical trials of prophylaxis have been criticized for using highly selected patient populations,¹¹ and prophylaxis QI studies show an inconsistent impact on VTE outcomes.¹⁵ In previous studies, two of the authors monitored orders for prophylaxis^22,24; during this project, delivery for both pharmacologic and mechanical VTE prophylaxis was monitored, confirming that patient care actually changed.

Our multicenter VTE prophylaxis initiative, featuring a “3-bucket” VTE protocol, QI mentorship, and active surveillance as key interventions, was associated with improved prophylaxis rates and a reduction in HA-VTE by 22% with no increase in adverse events. This project provides a model for hospital systems seeking to optimize their prophylaxis efforts, and it supports the use of collaborative QI initiatives and SHM’s quality mentorship program as methods to drive improvement across health systems.

Disclosure

None of the authors have any conflicts of interest related to any topics or products discussed in the article. Dignity Health provided a stipend for writing the manuscript to GM and IJ, as noted in the article, but had no role in data analysis, writing, or decision to submit.

Deep venous thrombosis and pulmonary embolism, collectively known as venous thromboembolism (VTE), affect up to 600,000 Americans a year.¹ Most of these are hospital-associated venous thromboembolisms (HA-VTE).^1,2 VTE poses a substantial risk of mortality and long-term morbidity, and its treatment poses a risk of major bleeding.¹ As appropriate VTE prophylaxis (“prophylaxis”) can reduce the risk of VTE by 40% to 80% depending on the patient population,³ VTE risk assessment and prophylaxis is endorsed by multiple guidelines^4-7 and supported by regulatory agencies.^8-10

However, despite extensive study, consensus about the impact of prophylaxis^4,11 and the optimal method of risk assessment^4,5,7,12 is lacking. Meanwhile, implementation of prophylaxis in real-world settings is poor; only 40% to 60% of at-risk patients receive prophylaxis,¹³ and as few as <20% receive optimal prophylaxis.¹⁴ Both systematic reviews^15,16 and experience with VTE prevention collaboratives^17,18 found that multifaceted interventions and alerts may be most effective in improving prophylaxis rates, but without proof of improved VTE rates.¹⁵ There is limited experience with large-scale VTE prevention. Organizations like The Joint Commission (TJC)⁸ and the Surgical Care Improvement Project have promoted quality measures but without clear evidence of improvement.¹⁹ In addition, an analysis of over 20,000 medical patients at 35 hospitals found no difference in VTE rates between high- and low-performing hospitals,²⁰ suggesting that aggressive prophylaxis efforts may not reduce VTE, at least among medical patients.²¹ However, a 5-hospital University of California collaborative was associated with improved VTE rates, chiefly among surgical patients.²²

In 2011, Dignity Health targeted VTE for improvement after investigations of potentially preventable HA-VTE revealed variable patterns of prophylaxis. In addition, improvement seemed feasible because there is a proven framework for VTE quality improvement (QI) projects^17,18 and a record of success with the following 3 specific strategies: quality mentorship,²³ use of a simple VTE risk assessment method, and active surveillance (real-time monitoring targeting suboptimal prophylaxis with concurrent intervention). This active surveillance technique has been used successfully in prior improvement efforts, often termed measure-vention.^17,18,22,24

Setting and Participants

The QI collaborative was performed at 35 Dignity Health community hospitals in California, Arizona, and Nevada. Facilities ranged from 25 to 571 beds in size with a mixture of teaching and nonteaching hospitals. Prior to the initiative, prophylaxis improvement efforts were incomplete and inconsistent at study facilities. All adult acute care inpatients at all facilities were included except rehabilitation, behavioral health, skilled nursing, hospice, other nonacute care, and inpatient deliveries.

Design Overview

We performed a prospective, unblinded, open-intervention study of a QI collaborative in 35 community hospitals and studied the effect on prophylaxis and VTE rates with historical controls. The 35 hospitals were organized into 2 cohorts. In the “pilot” cohort, 9 hospitals (chosen to be representative of the various settings, size, and teaching status within the Dignity system) received funding from the Gordon and Betty Moore Foundation (GBMF) for intensive, individualized QI mentorship from experts as well as active surveillance (see “Interventions”). The pilot sites led the development of the VTE risk assessment and prophylaxis protocol (“VTE protocol”), measures, order sets, implementation tactics, and lessons learned, assisted by the mentor experts. Dissemination to the 26-hospital “spread” cohort was facilitated by the Dignity Health Hospital Engagement Network (HEN) infrastructure.

Timeline

Two of the pilot sites, acting as leads on the development of protocol and order set tools, formed improvement teams in March 2011, 6 to 12 months earlier than other Dignity sites. Planning and design work occurred from March 2011 to September 2012. Most implementation at the 35 hospitals occurred in a staggered fashion during calendar year (CY) 2012 and 2013 (see Figure 1). As few changes were made until mid-2012, we considered CY 2011 the baseline for comparison, CY 2012 to 2013 the implementation years, and CY 2014 the postimplementation period.

The project was reviewed by the Institutional Review Board (IRB) of Dignity Health and determined to be an IRB-exempt QI project.

Interventions

Collaborative Infrastructure

Multidisciplinary Teams

Improvement teams formed between March 2011 and September 2012. Members included a physician champion, frontline nurses and physicians, an administrative liaison, pharmacists, quality and data specialists, clinical informatics staff, and stakeholders from key clinical services. Teams met at least monthly at each site.

Physician Mentors

The 9 pilot sites received individualized mentorship provided by outside experts (IJ or GM) based on a model pioneered by the Society of Hospital Medicine’s (SHM) Mentored Implementation programs.²³ Each pilot site completed a self-assessment survey¹⁷ (see supplementary Appendix A) about past efforts, team composition, current performance, aims, barriers, and opportunities. The mentors reviewed the completed questionnaire with each hospital and provided advice on the VTE protocol and order set design, measurement, and benchmarking during 3 webinar meetings scheduled at 0, 3, and 9 months, plus as-needed e-mail and phone correspondence. After each webinar, the mentors provided detailed improvement suggestions (see supplementary Appendix B). Several hospitals received mentor site visits, which focused on unit rounding, active surveillance, staff and provider education, and problem-solving sessions with senior leadership, physician leadership, and the improvement team.

VTE Protocol

After a literature review and consultation with the mentors, Dignity Health developed and implemented a VTE protocol, modified from a model used in previous improvement efforts.^18,22-24 Its risk assessment method is often referred to as a “3 bucket” model because it assigns patients to high-, moderate-, or low-risk categories based on clinical factors (eg, major orthopedic surgery, prior VTE, and others), and the VTE protocol recommends interventions based on the risk category (see supplementary Appendix C). Dignity Health was transitioning to a single electronic health record (Cerner Corporation, North Kansas City, MO) during the study, and study hospitals were using multiple platforms, necessitating the development of both paper and electronic versions of the VTE protocol. The electronic version required completion of the VTE protocol for all inpatient admissions and transfers. The VTE protocol was completed in November 2011 and disseminated to other sites in a staggered fashion through November 2012. Completed protocols and improvement tips were shared by the project lead and by webinar sessions. Sites were also encouraged to implement a standardized practice that allowed nurses to apply sequential compression devices to at-risk patients without physician orders when indicated by protocol, when contraindications such as vascular disease or ulceration were absent.

Education

Staff were educated about the VTE protocol by local teams, starting between late 2011 and September 2012. The audience (physicians, nurses, pharmacists, etc.) and methods (conferences, fliers, etc.) were determined by local teams, following guidance by mentors and webinar content. Active surveillance provided opportunities for in-the-moment, patient-specific education and protocol reinforcement. Both mentors delivered educational presentations at pilot sites.

Active Surveillance

Sites were encouraged to perform daily review of prophylaxis adequacy for inpatients and correct lapses in real time (both under- and overprophylaxis). Inappropriate prophylaxis orders were addressed by contacting providers to change the order or document the rationale not to. Lapses in adherence to prophylaxis were addressed by nursing correction and education of involved staff. Active surveillance was funded for 10 hours a week at pilot sites. Spread sites received only minimal support from HEN monies. All sites used daily prophylaxis reports, enhanced to include contraindications like thrombocytopenia and coagulopathy, to facilitate efforts. Active surveillance began in May 2012 in the lead pilot hospitals and was implemented in other sites between October 2012 and February 2013.

Metrics

Prophylaxis Rates

Measurement of prophylaxis did not begin until 2012 to 2013; thus, the true baseline rate for prophylaxis was not captured. TJC metrics (VTE-1 and VTE-2)²⁵ were consolidated into a composite TJC prophylaxis rate from January 2012 to December 2014 for both pilot and spread hospitals. These measures assess the percentage of adult inpatients who received VTE prophylaxis or have documentation of why no prophylaxis was given the day of or day after hospital admission (VTE-1) or the day of or day after ICU admission or transfer (VTE-2). These measures are met if any mechanical or pharmacologic prophylaxis was delivered.

In addition to the TJC metric, the 9 pilot hospitals monitored rates of protocol-compliant prophylaxis for 12 to 20 months. Each patient’s prophylaxis was considered protocol compliant if it was consistent with the prophylaxis protocol at the time of the audit or if contraindications were documented (eg, patients eligible for, but with contraindications to, pharmacologic prophylaxis had to have an order for mechanical prophylaxis or documented contraindication to both modalities). As this measure was initiated in a staggered fashion, the rate of protocol-compliant prophylaxis is summarized for consecutive months of measurement rather than consecutive calendar months.

HA-VTE Rates

VTE events were captured by review of electronic coding data for the International Classification of Diseases, 9th Revision (ICD-9) codes 415.11-415.19, 453.2, 453.40-453.42, and 453.8-453.89. HA-VTE was defined as either new VTE not present on admission (NPOA HA-VTE) or new VTE presenting in a readmitted patient within 30 days of discharge (Readmit HA-VTE). Cases were stratified based on whether the patient had undergone a major operation (surgery patients) or not (medical patients) as identified by Medicare Services diagnosis-related group codes.

Control Measures

Potential adverse events were captured by review of electronic coding data for ICD-9 codes 289.84 (heparin-induced thrombocytopenia [HIT]) and E934.2 (adverse effects because of anticoagulants).

Statistical Analysis

Statistical process control charts were used to depict changes in prophylaxis rates over the 3 years for which data was collected. For VTE and safety outcomes, Pearson χ² value with relative risk (RR) calculations and 95% confidence intervals (CIs) were used to compare proportions between groups at baseline (CY 2011) versus postimplementation (CY 2014). Differences between the means of normally distributed data were calculated, and a 95% CI for the difference between the means was performed to assess statistical difference. Nonparametric characteristics were described by quartiles and interquartile range, and the 2-sided Mann-Whitney U test was performed to assess statistical difference between the CY 2011 and CY 2014 period.

Role of the Funding Source

The GBMF funded the collaborative and supported authorship of the manuscript but had no role in the design or conduct of the intervention, the collection or analysis of data, or the drafting of the manuscript.

Population Demographics

There were 1,155,069 adult inpatient admissions during the 4-year study period (264,280 in the 9 pilot sites, 890,789 in the 26 spread sites). There were no clinically relevant changes in gender distribution, mortality rate, median age, case mix index, or hospital length of stay in 2011 versus 2014. Men comprised 47.1% of the patient population in 2011 and 47.7% in 2014. The mortality rate was 2.7% in both years. Median age was 62 in 2011 and 63 in 2014. The mean case mix index (1.58 vs 1.65) and mean length of stay (4.29 vs 4.33 days) were similar in the 2 time periods.

Prophylaxis Rates

TJC Prophylaxis rates

Rates of Protocol-Compliant Prophylaxis

There were 34,071 active surveillance audits across the 20 months of reporting in the pilot cohort (mean, 1817 audits per month). The rate of protocol-compliant prophylaxis improved from 89% at month 1 of observation to 93% during month 2 and 97% by the last 3 months (Pearson χ² P < .001 for both comparisons).

HA-VTE

HA-VTE characteristics

Improved HA-VTE over Time

Four hundred twenty-eight fewer HA-VTEs occurred in 2014 than in 2011 (RR 0.78; 95% CI, 0.73-0.85) (Table and Figure 3). Readmission HA-VTEs were reduced by 315 (RR 0.72; 95% CI, 0.65-0.80), while the reduction in NPOA HA-VTEs was less robust (RR 0.88; 95% CI, 0.79-0.99). Pilot sites enjoyed a more robust reduction in HA-VTEs than spread sites (26% vs 20%), largely because the pilot cohort enjoyed a 34% reduction in NPOA HA-VTEs and a 20% reduction in Readmit HA-VTEs, while the spread cohort only achieved reductions in Readmit HA-VTEs.

Safety

Rates of HIT and adverse effects because of anticoagulants were low (Table). The rate of HIT declined from 178 events in 2011 to 109 in 2014 (RR 0.66; 95% CI, 0.52-0.84), and the RR of anticoagulant adverse events remained stable (RR 1.01; 95% CI, 0.87-1.15).

Our QI project, based on a proven collaborative approach and mentorship,^18,22,24 order set redesign, and active surveillance, was associated with 26% less VTEs in the pilot cohort and 20% less VTEs in the spread cohort. These gains, down to a final rate of approximately 4 HA-VTEs per 1000 admissions, occurred despite a low baseline HA-VTE rate. Dignity Health achieved these improvements in 35 hospitals with varied sizes, settings, ordering systems, and teaching statuses, achieving what is to our knowledge the largest VTE QI initiative yet reported.

Implementation experiences were not systematically recorded, and techniques were not compared with a control group. However, we believe that Dignity Health’s organizational commitment to improvement and centralized support were crucial for success. In addition, the pilot sites received grant support from the GBMF for intensive quality mentoring, a strategy with demonstrated value.²³ Mentors and team members noted that system-wide revision to the computerized physician order entry system was easiest to implement, while active surveillance represented the most labor-intensive intervention. Other experiences echoed lessons from previous VTE mentorship efforts.^17,18

The selection of a VTE protocol conducive to implementation and provider use was a key strategy. The ideal approach to VTE risk assessment is not known,^12,26 but guidelines either offer no specific guidance⁷ or would require implementation of 3 different systems per hospital.^4,5 Several of these are point scoring systems, which may have lower clinician acceptance or require programming to improve real-world use^18,26,27; the Padua score was derived from a patient population that differs significantly from those in the United States.¹² Our study provides more practical experience with a “3-bucket” model, which has previously shown high interobserver reliability, good clinician acceptance, and meaningful reductions of VTE, including in American patient populations.^18,22,24

The value of VTE prophylaxis is still disputed in many inpatient groups. The overall rate of HA-VTE is low, so the per-patient benefit of prophylaxis is low, and many patients may be overprophylaxed.^4,11,12 Recently, Flanders et al.²⁰ reported that HA-VTE rates among 20,800 medical inpatients in Michigan were low (about 1%) and similar at hospitals in the top (mean prophylaxis rate 86%) or bottom (mean prophylaxis rate 56%) tertiles of performance. Possible explanations for the differences between their multicenter experience and ours include our sample size (55 times larger) and the possibility that targeting prophylaxis to patients at highest need (captured in our protocol-compliant prophylaxis rates) matters more than prophylaxing a percent of the population.

Further research is needed to develop simple, easy-to-implement methods to identify inpatients who do not, or no longer, require prophylaxis.¹² Hospital systems also need methods to determine if prophylaxis improvement efforts can lower their HA-VTE rates and in which subpopulations. For example, a collaborative effort at the University of California lowered HA-VTE rates toward a common improved rate of 0.65% to 0.73%,²² while Dignity Health achieved improvement despite starting with an even lower baseline. In the University of California collaborative, benefits were limited chiefly to surgical patients, while Dignity Health achieved most improvement in medical patients, particularly in Readmit HA-VTE. If future research uncovers the reasons for these differences, it could help hospitals decide where to target improvement efforts.

Our study has several limitations. First, we used a nonrandomized time series design, so we cannot exclude other potential explanations for the change in VTE rates. However, there were no major changes in patient populations or concurrent projects likely to have influenced event rates. While we did not collect detailed demographic information on subjects, the broad inclusion criteria and multicenter design suggests a high degree of generalizability. Second, we followed inpatient VTE events and VTE-related readmissions, but not VTE treated in the outpatient setting. This did not change over the study, but the availability of all-oral therapy for VTE could have caused underdetection if clinic or emergency room doctors sent home more patients on oral therapy instead of readmitting them to the hospital. Third, implementation was enhanced by GBMF funds (at 9 sites, with the remainder benefitting from their experience), a shared electronic medical record at many sites, and a strong organizational safety culture, which may limit generalizability. However, spread sites showed similar improvement, paper-based sites were included, and the mentorship and quality collaborative models are scalable at low cost. Fourth, some QI efforts began at some pilot sites in CY 2011, so we could not compare completely clean pre- and postproject timeframes. However, early improvement would have resulted in an underestimation of the project’s impact. Lastly, the reason for a decline in HIT rates is not known. Standardized order sets promoted preferential use of low molecular weight heparin, which is less likely to induce HIT, and active surveillance targeted overprophylaxis as well as underprophylaxis, but we do not have data on heparin utilization patterns to confirm or refute these possibilities.

Strengths of our study include reductions in HA-VTE, both with and without access to GBMF funds, by using broadly available QI strategies.¹⁷ This real-world success and ease of dissemination are particularly important because the clinical trials of prophylaxis have been criticized for using highly selected patient populations,¹¹ and prophylaxis QI studies show an inconsistent impact on VTE outcomes.¹⁵ In previous studies, two of the authors monitored orders for prophylaxis^22,24; during this project, delivery for both pharmacologic and mechanical VTE prophylaxis was monitored, confirming that patient care actually changed.

Our multicenter VTE prophylaxis initiative, featuring a “3-bucket” VTE protocol, QI mentorship, and active surveillance as key interventions, was associated with improved prophylaxis rates and a reduction in HA-VTE by 22% with no increase in adverse events. This project provides a model for hospital systems seeking to optimize their prophylaxis efforts, and it supports the use of collaborative QI initiatives and SHM’s quality mentorship program as methods to drive improvement across health systems.

Disclosure

None of the authors have any conflicts of interest related to any topics or products discussed in the article. Dignity Health provided a stipend for writing the manuscript to GM and IJ, as noted in the article, but had no role in data analysis, writing, or decision to submit.

Deep venous thrombosis and pulmonary embolism, collectively known as venous thromboembolism (VTE), affect up to 600,000 Americans a year.¹ Most of these are hospital-associated venous thromboembolisms (HA-VTE).^1,2 VTE poses a substantial risk of mortality and long-term morbidity, and its treatment poses a risk of major bleeding.¹ As appropriate VTE prophylaxis (“prophylaxis”) can reduce the risk of VTE by 40% to 80% depending on the patient population,³ VTE risk assessment and prophylaxis is endorsed by multiple guidelines^4-7 and supported by regulatory agencies.^8-10

However, despite extensive study, consensus about the impact of prophylaxis^4,11 and the optimal method of risk assessment^4,5,7,12 is lacking. Meanwhile, implementation of prophylaxis in real-world settings is poor; only 40% to 60% of at-risk patients receive prophylaxis,¹³ and as few as <20% receive optimal prophylaxis.¹⁴ Both systematic reviews^15,16 and experience with VTE prevention collaboratives^17,18 found that multifaceted interventions and alerts may be most effective in improving prophylaxis rates, but without proof of improved VTE rates.¹⁵ There is limited experience with large-scale VTE prevention. Organizations like The Joint Commission (TJC)⁸ and the Surgical Care Improvement Project have promoted quality measures but without clear evidence of improvement.¹⁹ In addition, an analysis of over 20,000 medical patients at 35 hospitals found no difference in VTE rates between high- and low-performing hospitals,²⁰ suggesting that aggressive prophylaxis efforts may not reduce VTE, at least among medical patients.²¹ However, a 5-hospital University of California collaborative was associated with improved VTE rates, chiefly among surgical patients.²²

In 2011, Dignity Health targeted VTE for improvement after investigations of potentially preventable HA-VTE revealed variable patterns of prophylaxis. In addition, improvement seemed feasible because there is a proven framework for VTE quality improvement (QI) projects^17,18 and a record of success with the following 3 specific strategies: quality mentorship,²³ use of a simple VTE risk assessment method, and active surveillance (real-time monitoring targeting suboptimal prophylaxis with concurrent intervention). This active surveillance technique has been used successfully in prior improvement efforts, often termed measure-vention.^17,18,22,24

Setting and Participants

The QI collaborative was performed at 35 Dignity Health community hospitals in California, Arizona, and Nevada. Facilities ranged from 25 to 571 beds in size with a mixture of teaching and nonteaching hospitals. Prior to the initiative, prophylaxis improvement efforts were incomplete and inconsistent at study facilities. All adult acute care inpatients at all facilities were included except rehabilitation, behavioral health, skilled nursing, hospice, other nonacute care, and inpatient deliveries.

Design Overview

We performed a prospective, unblinded, open-intervention study of a QI collaborative in 35 community hospitals and studied the effect on prophylaxis and VTE rates with historical controls. The 35 hospitals were organized into 2 cohorts. In the “pilot” cohort, 9 hospitals (chosen to be representative of the various settings, size, and teaching status within the Dignity system) received funding from the Gordon and Betty Moore Foundation (GBMF) for intensive, individualized QI mentorship from experts as well as active surveillance (see “Interventions”). The pilot sites led the development of the VTE risk assessment and prophylaxis protocol (“VTE protocol”), measures, order sets, implementation tactics, and lessons learned, assisted by the mentor experts. Dissemination to the 26-hospital “spread” cohort was facilitated by the Dignity Health Hospital Engagement Network (HEN) infrastructure.

Timeline

Two of the pilot sites, acting as leads on the development of protocol and order set tools, formed improvement teams in March 2011, 6 to 12 months earlier than other Dignity sites. Planning and design work occurred from March 2011 to September 2012. Most implementation at the 35 hospitals occurred in a staggered fashion during calendar year (CY) 2012 and 2013 (see Figure 1). As few changes were made until mid-2012, we considered CY 2011 the baseline for comparison, CY 2012 to 2013 the implementation years, and CY 2014 the postimplementation period.

The project was reviewed by the Institutional Review Board (IRB) of Dignity Health and determined to be an IRB-exempt QI project.

Interventions

Collaborative Infrastructure

Multidisciplinary Teams

Improvement teams formed between March 2011 and September 2012. Members included a physician champion, frontline nurses and physicians, an administrative liaison, pharmacists, quality and data specialists, clinical informatics staff, and stakeholders from key clinical services. Teams met at least monthly at each site.

Physician Mentors

The 9 pilot sites received individualized mentorship provided by outside experts (IJ or GM) based on a model pioneered by the Society of Hospital Medicine’s (SHM) Mentored Implementation programs.²³ Each pilot site completed a self-assessment survey¹⁷ (see supplementary Appendix A) about past efforts, team composition, current performance, aims, barriers, and opportunities. The mentors reviewed the completed questionnaire with each hospital and provided advice on the VTE protocol and order set design, measurement, and benchmarking during 3 webinar meetings scheduled at 0, 3, and 9 months, plus as-needed e-mail and phone correspondence. After each webinar, the mentors provided detailed improvement suggestions (see supplementary Appendix B). Several hospitals received mentor site visits, which focused on unit rounding, active surveillance, staff and provider education, and problem-solving sessions with senior leadership, physician leadership, and the improvement team.

VTE Protocol

After a literature review and consultation with the mentors, Dignity Health developed and implemented a VTE protocol, modified from a model used in previous improvement efforts.^18,22-24 Its risk assessment method is often referred to as a “3 bucket” model because it assigns patients to high-, moderate-, or low-risk categories based on clinical factors (eg, major orthopedic surgery, prior VTE, and others), and the VTE protocol recommends interventions based on the risk category (see supplementary Appendix C). Dignity Health was transitioning to a single electronic health record (Cerner Corporation, North Kansas City, MO) during the study, and study hospitals were using multiple platforms, necessitating the development of both paper and electronic versions of the VTE protocol. The electronic version required completion of the VTE protocol for all inpatient admissions and transfers. The VTE protocol was completed in November 2011 and disseminated to other sites in a staggered fashion through November 2012. Completed protocols and improvement tips were shared by the project lead and by webinar sessions. Sites were also encouraged to implement a standardized practice that allowed nurses to apply sequential compression devices to at-risk patients without physician orders when indicated by protocol, when contraindications such as vascular disease or ulceration were absent.

Education

Staff were educated about the VTE protocol by local teams, starting between late 2011 and September 2012. The audience (physicians, nurses, pharmacists, etc.) and methods (conferences, fliers, etc.) were determined by local teams, following guidance by mentors and webinar content. Active surveillance provided opportunities for in-the-moment, patient-specific education and protocol reinforcement. Both mentors delivered educational presentations at pilot sites.

Active Surveillance

Sites were encouraged to perform daily review of prophylaxis adequacy for inpatients and correct lapses in real time (both under- and overprophylaxis). Inappropriate prophylaxis orders were addressed by contacting providers to change the order or document the rationale not to. Lapses in adherence to prophylaxis were addressed by nursing correction and education of involved staff. Active surveillance was funded for 10 hours a week at pilot sites. Spread sites received only minimal support from HEN monies. All sites used daily prophylaxis reports, enhanced to include contraindications like thrombocytopenia and coagulopathy, to facilitate efforts. Active surveillance began in May 2012 in the lead pilot hospitals and was implemented in other sites between October 2012 and February 2013.

Metrics

Prophylaxis Rates

Measurement of prophylaxis did not begin until 2012 to 2013; thus, the true baseline rate for prophylaxis was not captured. TJC metrics (VTE-1 and VTE-2)²⁵ were consolidated into a composite TJC prophylaxis rate from January 2012 to December 2014 for both pilot and spread hospitals. These measures assess the percentage of adult inpatients who received VTE prophylaxis or have documentation of why no prophylaxis was given the day of or day after hospital admission (VTE-1) or the day of or day after ICU admission or transfer (VTE-2). These measures are met if any mechanical or pharmacologic prophylaxis was delivered.

In addition to the TJC metric, the 9 pilot hospitals monitored rates of protocol-compliant prophylaxis for 12 to 20 months. Each patient’s prophylaxis was considered protocol compliant if it was consistent with the prophylaxis protocol at the time of the audit or if contraindications were documented (eg, patients eligible for, but with contraindications to, pharmacologic prophylaxis had to have an order for mechanical prophylaxis or documented contraindication to both modalities). As this measure was initiated in a staggered fashion, the rate of protocol-compliant prophylaxis is summarized for consecutive months of measurement rather than consecutive calendar months.

HA-VTE Rates

VTE events were captured by review of electronic coding data for the International Classification of Diseases, 9th Revision (ICD-9) codes 415.11-415.19, 453.2, 453.40-453.42, and 453.8-453.89. HA-VTE was defined as either new VTE not present on admission (NPOA HA-VTE) or new VTE presenting in a readmitted patient within 30 days of discharge (Readmit HA-VTE). Cases were stratified based on whether the patient had undergone a major operation (surgery patients) or not (medical patients) as identified by Medicare Services diagnosis-related group codes.

Control Measures

Potential adverse events were captured by review of electronic coding data for ICD-9 codes 289.84 (heparin-induced thrombocytopenia [HIT]) and E934.2 (adverse effects because of anticoagulants).

Statistical Analysis

Statistical process control charts were used to depict changes in prophylaxis rates over the 3 years for which data was collected. For VTE and safety outcomes, Pearson χ² value with relative risk (RR) calculations and 95% confidence intervals (CIs) were used to compare proportions between groups at baseline (CY 2011) versus postimplementation (CY 2014). Differences between the means of normally distributed data were calculated, and a 95% CI for the difference between the means was performed to assess statistical difference. Nonparametric characteristics were described by quartiles and interquartile range, and the 2-sided Mann-Whitney U test was performed to assess statistical difference between the CY 2011 and CY 2014 period.

Role of the Funding Source

The GBMF funded the collaborative and supported authorship of the manuscript but had no role in the design or conduct of the intervention, the collection or analysis of data, or the drafting of the manuscript.

Population Demographics

There were 1,155,069 adult inpatient admissions during the 4-year study period (264,280 in the 9 pilot sites, 890,789 in the 26 spread sites). There were no clinically relevant changes in gender distribution, mortality rate, median age, case mix index, or hospital length of stay in 2011 versus 2014. Men comprised 47.1% of the patient population in 2011 and 47.7% in 2014. The mortality rate was 2.7% in both years. Median age was 62 in 2011 and 63 in 2014. The mean case mix index (1.58 vs 1.65) and mean length of stay (4.29 vs 4.33 days) were similar in the 2 time periods.

Prophylaxis Rates

TJC Prophylaxis rates

Rates of Protocol-Compliant Prophylaxis

There were 34,071 active surveillance audits across the 20 months of reporting in the pilot cohort (mean, 1817 audits per month). The rate of protocol-compliant prophylaxis improved from 89% at month 1 of observation to 93% during month 2 and 97% by the last 3 months (Pearson χ² P < .001 for both comparisons).

HA-VTE

HA-VTE characteristics

Improved HA-VTE over Time

Four hundred twenty-eight fewer HA-VTEs occurred in 2014 than in 2011 (RR 0.78; 95% CI, 0.73-0.85) (Table and Figure 3). Readmission HA-VTEs were reduced by 315 (RR 0.72; 95% CI, 0.65-0.80), while the reduction in NPOA HA-VTEs was less robust (RR 0.88; 95% CI, 0.79-0.99). Pilot sites enjoyed a more robust reduction in HA-VTEs than spread sites (26% vs 20%), largely because the pilot cohort enjoyed a 34% reduction in NPOA HA-VTEs and a 20% reduction in Readmit HA-VTEs, while the spread cohort only achieved reductions in Readmit HA-VTEs.

Safety

Rates of HIT and adverse effects because of anticoagulants were low (Table). The rate of HIT declined from 178 events in 2011 to 109 in 2014 (RR 0.66; 95% CI, 0.52-0.84), and the RR of anticoagulant adverse events remained stable (RR 1.01; 95% CI, 0.87-1.15).

Our QI project, based on a proven collaborative approach and mentorship,^18,22,24 order set redesign, and active surveillance, was associated with 26% less VTEs in the pilot cohort and 20% less VTEs in the spread cohort. These gains, down to a final rate of approximately 4 HA-VTEs per 1000 admissions, occurred despite a low baseline HA-VTE rate. Dignity Health achieved these improvements in 35 hospitals with varied sizes, settings, ordering systems, and teaching statuses, achieving what is to our knowledge the largest VTE QI initiative yet reported.

Implementation experiences were not systematically recorded, and techniques were not compared with a control group. However, we believe that Dignity Health’s organizational commitment to improvement and centralized support were crucial for success. In addition, the pilot sites received grant support from the GBMF for intensive quality mentoring, a strategy with demonstrated value.²³ Mentors and team members noted that system-wide revision to the computerized physician order entry system was easiest to implement, while active surveillance represented the most labor-intensive intervention. Other experiences echoed lessons from previous VTE mentorship efforts.^17,18

The selection of a VTE protocol conducive to implementation and provider use was a key strategy. The ideal approach to VTE risk assessment is not known,^12,26 but guidelines either offer no specific guidance⁷ or would require implementation of 3 different systems per hospital.^4,5 Several of these are point scoring systems, which may have lower clinician acceptance or require programming to improve real-world use^18,26,27; the Padua score was derived from a patient population that differs significantly from those in the United States.¹² Our study provides more practical experience with a “3-bucket” model, which has previously shown high interobserver reliability, good clinician acceptance, and meaningful reductions of VTE, including in American patient populations.^18,22,24

The value of VTE prophylaxis is still disputed in many inpatient groups. The overall rate of HA-VTE is low, so the per-patient benefit of prophylaxis is low, and many patients may be overprophylaxed.^4,11,12 Recently, Flanders et al.²⁰ reported that HA-VTE rates among 20,800 medical inpatients in Michigan were low (about 1%) and similar at hospitals in the top (mean prophylaxis rate 86%) or bottom (mean prophylaxis rate 56%) tertiles of performance. Possible explanations for the differences between their multicenter experience and ours include our sample size (55 times larger) and the possibility that targeting prophylaxis to patients at highest need (captured in our protocol-compliant prophylaxis rates) matters more than prophylaxing a percent of the population.

Further research is needed to develop simple, easy-to-implement methods to identify inpatients who do not, or no longer, require prophylaxis.¹² Hospital systems also need methods to determine if prophylaxis improvement efforts can lower their HA-VTE rates and in which subpopulations. For example, a collaborative effort at the University of California lowered HA-VTE rates toward a common improved rate of 0.65% to 0.73%,²² while Dignity Health achieved improvement despite starting with an even lower baseline. In the University of California collaborative, benefits were limited chiefly to surgical patients, while Dignity Health achieved most improvement in medical patients, particularly in Readmit HA-VTE. If future research uncovers the reasons for these differences, it could help hospitals decide where to target improvement efforts.

Our study has several limitations. First, we used a nonrandomized time series design, so we cannot exclude other potential explanations for the change in VTE rates. However, there were no major changes in patient populations or concurrent projects likely to have influenced event rates. While we did not collect detailed demographic information on subjects, the broad inclusion criteria and multicenter design suggests a high degree of generalizability. Second, we followed inpatient VTE events and VTE-related readmissions, but not VTE treated in the outpatient setting. This did not change over the study, but the availability of all-oral therapy for VTE could have caused underdetection if clinic or emergency room doctors sent home more patients on oral therapy instead of readmitting them to the hospital. Third, implementation was enhanced by GBMF funds (at 9 sites, with the remainder benefitting from their experience), a shared electronic medical record at many sites, and a strong organizational safety culture, which may limit generalizability. However, spread sites showed similar improvement, paper-based sites were included, and the mentorship and quality collaborative models are scalable at low cost. Fourth, some QI efforts began at some pilot sites in CY 2011, so we could not compare completely clean pre- and postproject timeframes. However, early improvement would have resulted in an underestimation of the project’s impact. Lastly, the reason for a decline in HIT rates is not known. Standardized order sets promoted preferential use of low molecular weight heparin, which is less likely to induce HIT, and active surveillance targeted overprophylaxis as well as underprophylaxis, but we do not have data on heparin utilization patterns to confirm or refute these possibilities.

Strengths of our study include reductions in HA-VTE, both with and without access to GBMF funds, by using broadly available QI strategies.¹⁷ This real-world success and ease of dissemination are particularly important because the clinical trials of prophylaxis have been criticized for using highly selected patient populations,¹¹ and prophylaxis QI studies show an inconsistent impact on VTE outcomes.¹⁵ In previous studies, two of the authors monitored orders for prophylaxis^22,24; during this project, delivery for both pharmacologic and mechanical VTE prophylaxis was monitored, confirming that patient care actually changed.

Our multicenter VTE prophylaxis initiative, featuring a “3-bucket” VTE protocol, QI mentorship, and active surveillance as key interventions, was associated with improved prophylaxis rates and a reduction in HA-VTE by 22% with no increase in adverse events. This project provides a model for hospital systems seeking to optimize their prophylaxis efforts, and it supports the use of collaborative QI initiatives and SHM’s quality mentorship program as methods to drive improvement across health systems.

Disclosure

None of the authors have any conflicts of interest related to any topics or products discussed in the article. Dignity Health provided a stipend for writing the manuscript to GM and IJ, as noted in the article, but had no role in data analysis, writing, or decision to submit.

Researchers are heading back to the drawing board after a tiny phase 1 trial of intrathecal rituximab in certain patients with progressive multiple sclerosis failed to eliminate inflammation in the meninges.

The study investigators had hoped the treatment would significantly help progressive MS patients with leptomeningeal inflammation, which has been linked to worsening disease. However, “this paradigm seems to have not gotten rid of the meningeal inflammation significantly. We need a better approach,” said Pavan Bhargava, MD, of Johns Hopkins University, Baltimore, who spoke in an interview in advance of presenting the study findings at a meeting held by the Americas Committee for Treatment and Research in Multiple Sclerosis.

“The main outcome was safety,” he said. “Most patients tolerated the rituximab, and the only side effect occurred in one patient who had abnormal sensations in the leg for 35-45 minutes. Otherwise, the medication was safe.”

The researchers detected no change in the meningeal inflammatory lesions before and after the treatment at 24 weeks.

But the tiny dose, much smaller than the typical two doses of 1,000 mg of IV rituximab over 2 weeks, completely depleted B cells in the blood of almost all patients, Dr. Bhargava said. “It was likely leaking out into the blood and clearing the B cells.” Peripheral blood B cells decreased from a median of 18.55% at baseline to 0.1% at week 2 and 8 with an increase to 3.6% at 24 weeks.

This effect was not as robust in spinal fluid, he said, where B cells were not fully depleted.

The findings suggest more cycles of treatment may be needed to target types of immune cells other than B cells, he noted. Or perhaps another drug would work better if it has a different mechanism and can penetrate more effectively.

The next step, he said, is to screen alternative drug approaches in animal models with meningeal inflammation that’s similar to that found in humans.

The study was funded by the International Progressive MS Alliance and the Race to Erase MS. Dr. Bhargava reported no relevant disclosures.

SOURCE: Bhargava P et al. ACTRIMS Forum 2018 Abstract P027.

Researchers are heading back to the drawing board after a tiny phase 1 trial of intrathecal rituximab in certain patients with progressive multiple sclerosis failed to eliminate inflammation in the meninges.

The study investigators had hoped the treatment would significantly help progressive MS patients with leptomeningeal inflammation, which has been linked to worsening disease. However, “this paradigm seems to have not gotten rid of the meningeal inflammation significantly. We need a better approach,” said Pavan Bhargava, MD, of Johns Hopkins University, Baltimore, who spoke in an interview in advance of presenting the study findings at a meeting held by the Americas Committee for Treatment and Research in Multiple Sclerosis.

“The main outcome was safety,” he said. “Most patients tolerated the rituximab, and the only side effect occurred in one patient who had abnormal sensations in the leg for 35-45 minutes. Otherwise, the medication was safe.”

The researchers detected no change in the meningeal inflammatory lesions before and after the treatment at 24 weeks.

But the tiny dose, much smaller than the typical two doses of 1,000 mg of IV rituximab over 2 weeks, completely depleted B cells in the blood of almost all patients, Dr. Bhargava said. “It was likely leaking out into the blood and clearing the B cells.” Peripheral blood B cells decreased from a median of 18.55% at baseline to 0.1% at week 2 and 8 with an increase to 3.6% at 24 weeks.

This effect was not as robust in spinal fluid, he said, where B cells were not fully depleted.

The findings suggest more cycles of treatment may be needed to target types of immune cells other than B cells, he noted. Or perhaps another drug would work better if it has a different mechanism and can penetrate more effectively.

The next step, he said, is to screen alternative drug approaches in animal models with meningeal inflammation that’s similar to that found in humans.

The study was funded by the International Progressive MS Alliance and the Race to Erase MS. Dr. Bhargava reported no relevant disclosures.

SOURCE: Bhargava P et al. ACTRIMS Forum 2018 Abstract P027.

Researchers are heading back to the drawing board after a tiny phase 1 trial of intrathecal rituximab in certain patients with progressive multiple sclerosis failed to eliminate inflammation in the meninges.

The study investigators had hoped the treatment would significantly help progressive MS patients with leptomeningeal inflammation, which has been linked to worsening disease. However, “this paradigm seems to have not gotten rid of the meningeal inflammation significantly. We need a better approach,” said Pavan Bhargava, MD, of Johns Hopkins University, Baltimore, who spoke in an interview in advance of presenting the study findings at a meeting held by the Americas Committee for Treatment and Research in Multiple Sclerosis.

“The main outcome was safety,” he said. “Most patients tolerated the rituximab, and the only side effect occurred in one patient who had abnormal sensations in the leg for 35-45 minutes. Otherwise, the medication was safe.”

The researchers detected no change in the meningeal inflammatory lesions before and after the treatment at 24 weeks.

But the tiny dose, much smaller than the typical two doses of 1,000 mg of IV rituximab over 2 weeks, completely depleted B cells in the blood of almost all patients, Dr. Bhargava said. “It was likely leaking out into the blood and clearing the B cells.” Peripheral blood B cells decreased from a median of 18.55% at baseline to 0.1% at week 2 and 8 with an increase to 3.6% at 24 weeks.

This effect was not as robust in spinal fluid, he said, where B cells were not fully depleted.

The findings suggest more cycles of treatment may be needed to target types of immune cells other than B cells, he noted. Or perhaps another drug would work better if it has a different mechanism and can penetrate more effectively.

The next step, he said, is to screen alternative drug approaches in animal models with meningeal inflammation that’s similar to that found in humans.

The study was funded by the International Progressive MS Alliance and the Race to Erase MS. Dr. Bhargava reported no relevant disclosures.

SOURCE: Bhargava P et al. ACTRIMS Forum 2018 Abstract P027.

The day had gone very well. The vascular surgeon woke early excited for a morning in the OR and then an afternoon in the office. Driving to the hospital, he had planned out his day. A patient with a fempop at 7:30, an AV fistula at 10:30 am, a quick bite in the doctor’s lounge, and then to the office for two phlebectomies, a few new consults, as well as some returning patients.

Fortunately, he had purchased an advanced electronic medical record so that reviewing old records and inputting new data went smoothly. He had been on call for the local hospital’s ER, but he received no calls, so his day was not impacted. After a dinner with his wife, also a surgeon, he helped put their youngest baby to sleep, played with his older children, took the dog out for a walk, read the latest JVS and went to sleep. Despite being on call, the phone never rang, and he had an uninterrupted sleep.

Now, what really happened!

The vascular surgeon woke early in preparation for a day in the OR and office. Traffic slowed him down, but he still arrived at the hospital just before his 7:30 start time. He expected his patient to be on the table prepped and ready for the procedure. But the OR supervisor informed him that new regulations required him to personally mark the site of surgery, update the H&P, and date and time the consent.

He was nonplussed. He had marked the patient last night and had signed the consent too. His PA had dictated a three-page H&P that was in the chart. However, the patient was still in the holding room. The surgeon rushed over, marked the leg again, and completed the required documentation.

“Well,” he thought, “I’ll run upstairs, discharge my carotid from yesterday, and by the time that’s done and I’ve changed into scrubs, my patient will be ready.” Impatiently he waited 5 minutes for the elevator, but it never arrived. So he elected to run up 10 floors and across to the other side of the hospital where the administrators had inconveniently placed the postop vascular patients. The patient was eager to leave. The vascular surgeon dictated the discharge note and signed into the hospital electronic medical record.

But the software insisted that he had to comply with numerous “safety” regulations before signing off. These required reviewing every medication and all discharge instructions. The patient was on 15 drugs, and the surgeon was unfamiliar with most. After 10 minutes of unsuccessfully trying to enter the relevant orders, he called a medical student over to help.

The patient was going to a skilled nursing facility. This required completing two more electronic forms. The software stubbornly refused to close the discharge section till he assigned the appropriate ICD-10 codes. After a few more frustrating minutes he finally clicked the proper boxes and completed the discharge.
 

It was 8:15 by the time he made the skin incision. The case went smoothly. He relaxed a little knowing that he probably would not run too late for the rest of his day. Finishing ahead of schedule, he dictated the note, spoke to the patient’s family, and went to preop his AV fistula scheduled for 10:30. Then back to the wards to complete rounds.

The first two patients were uncomplicated. The third had a fever requiring multiple orders in the EMR. Then heated conversations with the pharmacist and head of infection control, since the EMR would not allow him to prescribe the antibiotic of his choice. Back across the entire length of the hospital to see a patient with renal failure. But she was in dialysis at another distant location in the hospital.

At 10:30 he ran down to the OR ready to scrub. Again, this patient was still in the holding area. The patient’s potassium was 5.6, and the anesthesiologist wanted to run another blood test. Then the nurse had to go on break. Now there was confusion about whether a room would be available as another surgeon had a bump case.

Ultimately, he started at 11:30. During the procedure, his beeper went off constantly. There were already two consults in the ER. The fistula took a mere 30 minutes, but he had waited 90 minutes since finishing the fempop.

“Medicare should pay me for the time between cases, and I’ll do the procedure for free” he complained to a colleague as he passed her on the way to the ER to see the consults.

He sent the patient with the DVT home, but the patient with the infected foot would require later debridement. He admitted her and booked the OR for after office hours.

By the time he got to the doctors’ lounge all that was left was a half-eaten pack of Doritos and burned coffee.

He thought he would have a brief respite driving to the office. Then his surgeon wife called him in the car asking him to field a call from their son’s school since she was stuck in the OR.

He arrived late to the office. The waiting room was filled with hostile-looking patients one of whom made a point of holding up her watch as if to reinforce his tardiness. There were already three additions to his schedule. Further, his nurse told him that there was some issue with the internet connection to the server. Thus, despite his expensive EMR, no records were available. She had informed the patients that there would be a “little” delay.

While they were waiting she brought in reams of documents that had come in the prior day and needed his signatures. He also used the time “productively” to answer emails. When the EMR was back online, he returned to his patients who by now were seething.

A patient brought in a CD of a CTA. He loaded it up on a computer, but the disc kept spinning relentlessly. Cursing, he loaded it on a second computer. The instructions were indecipherable. He could get a picture up but could not scroll through the images. The program froze. By the time he had evaluated the disc, he had wasted over 20 minutes. He was running even further behind.

The next patient was a second opinion from a physician in another state. She brought in over 200 pages of medical records describing a multitude of prior procedures. Politely he explained he would need to read them first and rescheduled her.

The ER called again with a patient with a cold leg. He canceled the rest of the office and snuck out through a back door, afraid to witness the consternation in the waiting room.

At the hospital, he argued briefly with the anesthesiologist who was reluctant to anesthetize the patient who had eaten 5 hours before. So the harried surgeon read some vascular labs, and visited a few less stable patients. Then back to the OR to revascularize the ER patient’s leg and later to debride the earlier patient’s foot.

He got home at 8:30 pm. His wife had also been delayed by a long surgery. They put the baby to bed. There was no time to play with the other children. The surgical couple barely had the energy left to microwave leftovers for dinner. He was too tired to take the dog out for its nocturnal pee. He went to his study, picked up the JVS, and fell asleep in his chair. He woke up with a start as he felt the dog urinate on his leg.

Exhausted he climbed into bed. It had been a good day, he told himself. After all the ER had not been too disruptive. He drifted off into a deep sleep. And then the phone rang. Ruptured AAA in the ER.

The day had gone very well. The vascular surgeon woke early excited for a morning in the OR and then an afternoon in the office. Driving to the hospital, he had planned out his day. A patient with a fempop at 7:30, an AV fistula at 10:30 am, a quick bite in the doctor’s lounge, and then to the office for two phlebectomies, a few new consults, as well as some returning patients.

Fortunately, he had purchased an advanced electronic medical record so that reviewing old records and inputting new data went smoothly. He had been on call for the local hospital’s ER, but he received no calls, so his day was not impacted. After a dinner with his wife, also a surgeon, he helped put their youngest baby to sleep, played with his older children, took the dog out for a walk, read the latest JVS and went to sleep. Despite being on call, the phone never rang, and he had an uninterrupted sleep.

Now, what really happened!

The vascular surgeon woke early in preparation for a day in the OR and office. Traffic slowed him down, but he still arrived at the hospital just before his 7:30 start time. He expected his patient to be on the table prepped and ready for the procedure. But the OR supervisor informed him that new regulations required him to personally mark the site of surgery, update the H&P, and date and time the consent.

He was nonplussed. He had marked the patient last night and had signed the consent too. His PA had dictated a three-page H&P that was in the chart. However, the patient was still in the holding room. The surgeon rushed over, marked the leg again, and completed the required documentation.

“Well,” he thought, “I’ll run upstairs, discharge my carotid from yesterday, and by the time that’s done and I’ve changed into scrubs, my patient will be ready.” Impatiently he waited 5 minutes for the elevator, but it never arrived. So he elected to run up 10 floors and across to the other side of the hospital where the administrators had inconveniently placed the postop vascular patients. The patient was eager to leave. The vascular surgeon dictated the discharge note and signed into the hospital electronic medical record.

But the software insisted that he had to comply with numerous “safety” regulations before signing off. These required reviewing every medication and all discharge instructions. The patient was on 15 drugs, and the surgeon was unfamiliar with most. After 10 minutes of unsuccessfully trying to enter the relevant orders, he called a medical student over to help.

The patient was going to a skilled nursing facility. This required completing two more electronic forms. The software stubbornly refused to close the discharge section till he assigned the appropriate ICD-10 codes. After a few more frustrating minutes he finally clicked the proper boxes and completed the discharge.
 

It was 8:15 by the time he made the skin incision. The case went smoothly. He relaxed a little knowing that he probably would not run too late for the rest of his day. Finishing ahead of schedule, he dictated the note, spoke to the patient’s family, and went to preop his AV fistula scheduled for 10:30. Then back to the wards to complete rounds.

The first two patients were uncomplicated. The third had a fever requiring multiple orders in the EMR. Then heated conversations with the pharmacist and head of infection control, since the EMR would not allow him to prescribe the antibiotic of his choice. Back across the entire length of the hospital to see a patient with renal failure. But she was in dialysis at another distant location in the hospital.

At 10:30 he ran down to the OR ready to scrub. Again, this patient was still in the holding area. The patient’s potassium was 5.6, and the anesthesiologist wanted to run another blood test. Then the nurse had to go on break. Now there was confusion about whether a room would be available as another surgeon had a bump case.

Ultimately, he started at 11:30. During the procedure, his beeper went off constantly. There were already two consults in the ER. The fistula took a mere 30 minutes, but he had waited 90 minutes since finishing the fempop.

“Medicare should pay me for the time between cases, and I’ll do the procedure for free” he complained to a colleague as he passed her on the way to the ER to see the consults.

He sent the patient with the DVT home, but the patient with the infected foot would require later debridement. He admitted her and booked the OR for after office hours.

By the time he got to the doctors’ lounge all that was left was a half-eaten pack of Doritos and burned coffee.

He thought he would have a brief respite driving to the office. Then his surgeon wife called him in the car asking him to field a call from their son’s school since she was stuck in the OR.

He arrived late to the office. The waiting room was filled with hostile-looking patients one of whom made a point of holding up her watch as if to reinforce his tardiness. There were already three additions to his schedule. Further, his nurse told him that there was some issue with the internet connection to the server. Thus, despite his expensive EMR, no records were available. She had informed the patients that there would be a “little” delay.

While they were waiting she brought in reams of documents that had come in the prior day and needed his signatures. He also used the time “productively” to answer emails. When the EMR was back online, he returned to his patients who by now were seething.

A patient brought in a CD of a CTA. He loaded it up on a computer, but the disc kept spinning relentlessly. Cursing, he loaded it on a second computer. The instructions were indecipherable. He could get a picture up but could not scroll through the images. The program froze. By the time he had evaluated the disc, he had wasted over 20 minutes. He was running even further behind.

The next patient was a second opinion from a physician in another state. She brought in over 200 pages of medical records describing a multitude of prior procedures. Politely he explained he would need to read them first and rescheduled her.

The ER called again with a patient with a cold leg. He canceled the rest of the office and snuck out through a back door, afraid to witness the consternation in the waiting room.

At the hospital, he argued briefly with the anesthesiologist who was reluctant to anesthetize the patient who had eaten 5 hours before. So the harried surgeon read some vascular labs, and visited a few less stable patients. Then back to the OR to revascularize the ER patient’s leg and later to debride the earlier patient’s foot.

He got home at 8:30 pm. His wife had also been delayed by a long surgery. They put the baby to bed. There was no time to play with the other children. The surgical couple barely had the energy left to microwave leftovers for dinner. He was too tired to take the dog out for its nocturnal pee. He went to his study, picked up the JVS, and fell asleep in his chair. He woke up with a start as he felt the dog urinate on his leg.

Exhausted he climbed into bed. It had been a good day, he told himself. After all the ER had not been too disruptive. He drifted off into a deep sleep. And then the phone rang. Ruptured AAA in the ER.

The day had gone very well. The vascular surgeon woke early excited for a morning in the OR and then an afternoon in the office. Driving to the hospital, he had planned out his day. A patient with a fempop at 7:30, an AV fistula at 10:30 am, a quick bite in the doctor’s lounge, and then to the office for two phlebectomies, a few new consults, as well as some returning patients.

Fortunately, he had purchased an advanced electronic medical record so that reviewing old records and inputting new data went smoothly. He had been on call for the local hospital’s ER, but he received no calls, so his day was not impacted. After a dinner with his wife, also a surgeon, he helped put their youngest baby to sleep, played with his older children, took the dog out for a walk, read the latest JVS and went to sleep. Despite being on call, the phone never rang, and he had an uninterrupted sleep.

Now, what really happened!

The vascular surgeon woke early in preparation for a day in the OR and office. Traffic slowed him down, but he still arrived at the hospital just before his 7:30 start time. He expected his patient to be on the table prepped and ready for the procedure. But the OR supervisor informed him that new regulations required him to personally mark the site of surgery, update the H&P, and date and time the consent.

He was nonplussed. He had marked the patient last night and had signed the consent too. His PA had dictated a three-page H&P that was in the chart. However, the patient was still in the holding room. The surgeon rushed over, marked the leg again, and completed the required documentation.

“Well,” he thought, “I’ll run upstairs, discharge my carotid from yesterday, and by the time that’s done and I’ve changed into scrubs, my patient will be ready.” Impatiently he waited 5 minutes for the elevator, but it never arrived. So he elected to run up 10 floors and across to the other side of the hospital where the administrators had inconveniently placed the postop vascular patients. The patient was eager to leave. The vascular surgeon dictated the discharge note and signed into the hospital electronic medical record.

But the software insisted that he had to comply with numerous “safety” regulations before signing off. These required reviewing every medication and all discharge instructions. The patient was on 15 drugs, and the surgeon was unfamiliar with most. After 10 minutes of unsuccessfully trying to enter the relevant orders, he called a medical student over to help.

The patient was going to a skilled nursing facility. This required completing two more electronic forms. The software stubbornly refused to close the discharge section till he assigned the appropriate ICD-10 codes. After a few more frustrating minutes he finally clicked the proper boxes and completed the discharge.
 

It was 8:15 by the time he made the skin incision. The case went smoothly. He relaxed a little knowing that he probably would not run too late for the rest of his day. Finishing ahead of schedule, he dictated the note, spoke to the patient’s family, and went to preop his AV fistula scheduled for 10:30. Then back to the wards to complete rounds.

The first two patients were uncomplicated. The third had a fever requiring multiple orders in the EMR. Then heated conversations with the pharmacist and head of infection control, since the EMR would not allow him to prescribe the antibiotic of his choice. Back across the entire length of the hospital to see a patient with renal failure. But she was in dialysis at another distant location in the hospital.

At 10:30 he ran down to the OR ready to scrub. Again, this patient was still in the holding area. The patient’s potassium was 5.6, and the anesthesiologist wanted to run another blood test. Then the nurse had to go on break. Now there was confusion about whether a room would be available as another surgeon had a bump case.

Ultimately, he started at 11:30. During the procedure, his beeper went off constantly. There were already two consults in the ER. The fistula took a mere 30 minutes, but he had waited 90 minutes since finishing the fempop.

“Medicare should pay me for the time between cases, and I’ll do the procedure for free” he complained to a colleague as he passed her on the way to the ER to see the consults.

He sent the patient with the DVT home, but the patient with the infected foot would require later debridement. He admitted her and booked the OR for after office hours.

By the time he got to the doctors’ lounge all that was left was a half-eaten pack of Doritos and burned coffee.

He thought he would have a brief respite driving to the office. Then his surgeon wife called him in the car asking him to field a call from their son’s school since she was stuck in the OR.

He arrived late to the office. The waiting room was filled with hostile-looking patients one of whom made a point of holding up her watch as if to reinforce his tardiness. There were already three additions to his schedule. Further, his nurse told him that there was some issue with the internet connection to the server. Thus, despite his expensive EMR, no records were available. She had informed the patients that there would be a “little” delay.

While they were waiting she brought in reams of documents that had come in the prior day and needed his signatures. He also used the time “productively” to answer emails. When the EMR was back online, he returned to his patients who by now were seething.

A patient brought in a CD of a CTA. He loaded it up on a computer, but the disc kept spinning relentlessly. Cursing, he loaded it on a second computer. The instructions were indecipherable. He could get a picture up but could not scroll through the images. The program froze. By the time he had evaluated the disc, he had wasted over 20 minutes. He was running even further behind.

The next patient was a second opinion from a physician in another state. She brought in over 200 pages of medical records describing a multitude of prior procedures. Politely he explained he would need to read them first and rescheduled her.

The ER called again with a patient with a cold leg. He canceled the rest of the office and snuck out through a back door, afraid to witness the consternation in the waiting room.

At the hospital, he argued briefly with the anesthesiologist who was reluctant to anesthetize the patient who had eaten 5 hours before. So the harried surgeon read some vascular labs, and visited a few less stable patients. Then back to the OR to revascularize the ER patient’s leg and later to debride the earlier patient’s foot.

He got home at 8:30 pm. His wife had also been delayed by a long surgery. They put the baby to bed. There was no time to play with the other children. The surgical couple barely had the energy left to microwave leftovers for dinner. He was too tired to take the dog out for its nocturnal pee. He went to his study, picked up the JVS, and fell asleep in his chair. He woke up with a start as he felt the dog urinate on his leg.

Exhausted he climbed into bed. It had been a good day, he told himself. After all the ER had not been too disruptive. He drifted off into a deep sleep. And then the phone rang. Ruptured AAA in the ER.

In this podcast, Dr Henry discusses research articles on analgesic management in radiation oncology for painful bone metastases, hospitalizations for fracture in patients with metastatic disease, measurement of physical activity and sedentary behavior in breast cancer survivors, and patient navigators’ personal experiences with cancer and their impact on treatment. He also highlights a New Therapies review article on hallmark tumor metabolism becoming a validated therapeutic target, as well as The JCSO Interview, in which Kenneth Anderson, MD, talks about new myeloma drugs and their associated improved response rates and extended survival. Comments on the approvals of abemaciclib for breast cancer and avelumab and durvalumab for metastatic bladder cancer, and Case Reports on concurrent ipilimumab and CMV colitis refractory to oral steroids, recurrent head and neck cancer presenting as a large retroperitoneal mass, massive liver metastasis from colon adenocarcinoma causing cardiac tamponade, and cardiac pleomorphic sarcoma after placement of Dacron graft round out the line-up of articles. 

Listen to the podcast below.

In this podcast, Dr Henry discusses research articles on analgesic management in radiation oncology for painful bone metastases, hospitalizations for fracture in patients with metastatic disease, measurement of physical activity and sedentary behavior in breast cancer survivors, and patient navigators’ personal experiences with cancer and their impact on treatment. He also highlights a New Therapies review article on hallmark tumor metabolism becoming a validated therapeutic target, as well as The JCSO Interview, in which Kenneth Anderson, MD, talks about new myeloma drugs and their associated improved response rates and extended survival. Comments on the approvals of abemaciclib for breast cancer and avelumab and durvalumab for metastatic bladder cancer, and Case Reports on concurrent ipilimumab and CMV colitis refractory to oral steroids, recurrent head and neck cancer presenting as a large retroperitoneal mass, massive liver metastasis from colon adenocarcinoma causing cardiac tamponade, and cardiac pleomorphic sarcoma after placement of Dacron graft round out the line-up of articles. 

Listen to the podcast below.

In this podcast, Dr Henry discusses research articles on analgesic management in radiation oncology for painful bone metastases, hospitalizations for fracture in patients with metastatic disease, measurement of physical activity and sedentary behavior in breast cancer survivors, and patient navigators’ personal experiences with cancer and their impact on treatment. He also highlights a New Therapies review article on hallmark tumor metabolism becoming a validated therapeutic target, as well as The JCSO Interview, in which Kenneth Anderson, MD, talks about new myeloma drugs and their associated improved response rates and extended survival. Comments on the approvals of abemaciclib for breast cancer and avelumab and durvalumab for metastatic bladder cancer, and Case Reports on concurrent ipilimumab and CMV colitis refractory to oral steroids, recurrent head and neck cancer presenting as a large retroperitoneal mass, massive liver metastasis from colon adenocarcinoma causing cardiac tamponade, and cardiac pleomorphic sarcoma after placement of Dacron graft round out the line-up of articles. 

Listen to the podcast below.

The “Things We Do for No Reason” (TWDFNR) series reviews practices that have become common parts of hospital care but may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards, but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/

Chest pain is one of the most common complaints among patients presenting to the emergency department. Moreover, at least 30% of patients who present with chest pain are admitted for observation, and >70% of those admitted with chest pain undergo cardiac stress testing (CST) during hospitalization. Several clinical risk prediction models have validated evaluation processes for managing patients with chest pain, helping to identify those at a low risk of major adverse cardiac events. Among these, the Thrombolysis in Myocardial Infarction or HEART score can identify patients safe to be discharged with outpatient CST within 72 h. It is unnecessary to hospitalize all low-risk patients for cardiac testing because it may expose them to needless risk and avoidable care costs, with little additional benefit.

A 60-year-old man with a history of osteoarthritis and depression presented to our emergency department (ED) with a 1-month history of left-sided chest pain that was present both at rest and exertion. There were no aggravating or relieving factors for the pain and no associated shortness of breath, diaphoresis, nausea, or lightheadedness. He smoked a half pack of cigarettes daily for 5 years in his twenties. The patient was taking aspirin 81 mg daily and paroxetine 40 mg daily, which he had been taking for 10 years. There was a family history of coronary artery disease in his mother, father, and sister. On examination, he was afebrile, with a blood pressure of 138/78 mm Hg and a heart rate of 62 beats/min; he appeared well, with no abnormal cardiopulmonary findings. Investigation revealed a normal initial troponin I level (<0.034 mg/mL) and normal electrocardiogram (ECG) with normal sinus rhythm (75 beats/min), normal axis, no ST changes, and no Q waves. He was therefore admitted to the hospital for further evaluation.

Each year, >7 million patients visit ED for chest pain in the United States,¹ with approximately 13% diagnosed with acute coronary syndromes (ACSs).² Over 30% of patients who present to ED with chest pain are hospitalized for observation, symptom evaluation, and risk stratification.³ In 2012, the mean Medicare reimbursement cost was $1,741 for in-hospital observation,⁴ with up to 70% of admitted patients undergoing cardiac stress testing (CST) before discharge.⁵

A scientific statement by the American Heart Association in 2010 recommended that patients considered to be at low risk for ACS after initial evaluation (based on presenting symptoms, past history, ECG findings, and initial cardiac biomarkers) should undergo CST within 72 h (preferably within 24 h) of presentation to provoke ischemia or detect anatomic coronary artery disease.⁶ Early exercise treadmill testing as part of an accelerated diagnostic pathway can also reduce the length of stays (LOS) in hospital and lower the medical costs.⁷ Moreover, when there is noncompliance or poor accessibility, failure to pursue early exercise testing in a hospital could result in a loss of patients to follow-up. Hospitalization for testing through accelerated diagnostic pathways may improve access to care and reduce clinical and legal risks associated with a major adverse cardiac event (MACE).

Clinical Risk Prediction Models

When a patient initially presents with chest pain, it should be determined if the symptoms are related to ACS or some other diagnosis. Hospitalization is required for patients with ACS but may not be for those without ACS and those with a low risk of inducible ischemia. Clinical risk scores and risk prediction models, such as the Thrombolysis in Myocardial Infarction (TIMI) and HEART scores, have been used in accelerated diagnostic protocols to determine a patient’s likelihood of having ACS. Several large trials of these clinical risk prediction models have validated the processes for evaluating patients with chest pain.

The TIMI risk score, the most well-known model, assesses risk based on the presence or absence of 7 characteristics (Appendix 1). It should be noted that the patient population studied for initial validation of this model comprised high-risk patients with unstable angina or non-ST elevation myocardial infarction who would benefit from early or urgent invasive therapy.⁸ In this population, TIMI scores of 0-1 are associated with low risk, with a 4.7% risk of ACS at 14 days.⁸ In another study of patients presenting to ED with undifferentiated chest pain and a TIMI score of zero, the risk of MACE at 30 days was approximately 2%.⁹

The HEART score is also used for patients presenting to ED with undifferentiated chest pain and assesses 5 separate variables scored 0–2 (Appendix 2). The original research gave a score of 2 to a troponin I level greater than twice the upper limit of the normal level,¹⁰ whereas a subsequent validation study gave a score of 2 to a troponin I or T level greater than or equal to 3 times the upper limit of the normal level.¹¹ Patients are considered at low, intermediate, and high risk based on scores of 0–3, 4–6, and 7–10, respectively.^10,11 Backus et al. performed a prospective randomized trial of 2388 patients who presented to ED with chest pain to validate the HEART score and compare it to the TIMI risk score. The HEART score performed better than the TIMI risk score in low-risk patients, with TIMI scores of 0-1 and HEART scores of 0–3 having a 6-week MACE risk of 2.8% and 1.7%, respectively.¹¹

A HEART pathway was developed that combines the HEART score with serial troponin I assays assessed at the time of initial presentation and approximately 3 h later.¹² Mahler et al. randomized 282 patients presenting to ED with chest pain to either the HEART pathway or conventional care. Patients with low-risk HEART scores and an abnormal troponin I level were admitted for cardiology consultation, whereas discharge was recommended for those with low scores and a normal troponin I level. Despite nearly 20% of the study cohort having a history of myocardial infarction, percutaneous coronary intervention, or coronary artery bypass grafting, approximately 40% of patients in the HEART pathway were identified as low risk, increasing early discharge rates by 21.3% and decreasing the average LOS by 12 h. No low-risk patient suffered a MACE within 30 days, and the HEART pathway had a sensitivity and a negative predictive value of approximately 99%.

Costs and Harms of Hospitalization for Cardiac Testing

Hospitalization carries measurable risks.^13,14 Between 2008 and 2013, Weinstock et al. evaluated the outcomes of patients presenting with chest pain who were placed in an observation unit for suspected ACS.¹⁵ Low-risk patients were defined as those with normal ECGs (no ischemic changes), 2 negative troponin tests performed 60–420 min apart (no particular troponin assay specified), and stable vital signs. They identified 7266 patients who were considered to have low risk, among whom 4 (0.06%) had an adverse outcome in the hospital (eg, life-threatening arrhythmia, ST-segment elevation myocardial infarction, cardiac or respiratory arrest, or death); 3 among the 4 patients had a cardiac-related adverse outcome. The overall risk of adverse outcomes due to cardiac causes was 1 in 2422 admissions (0.04%). The authors compared their results with the reported risk of 1 in 164 admissions for preventable adverse events contributing to patient death during routine hospitalization (eg, medication or procedure errors).¹⁴

Outpatient CST can be reliably and safely performed for patients with chest pain.^16-18 There is no clear evidence that earlier CST leads to improved patient outcomes, and CST in the absence of acute ischemia (or ACS) increases the rates of angiography and revascularization without improvements in the rate of myocardial infarction.^19-21 Given the costs of in-hospital observation⁴ and the dubious benefits of providing CST for patients with low-risk chest pain, admitting all patients with low-risk chest pain exposes them to costs and harms with little potential benefit.

Patients presenting with chest pain with either dynamic ECG changes or an elevated troponin level require hospitalization for further ACS diagnosis and treatment. When ACS cannot be clearly diagnosed at the initial evaluation, healthcare providers should use clinical risk prediction models to stratify patients. Those deemed to be at an intermediate or high risk by these models should be hospitalized for further evaluation, as should those at low risk but for whom access to outpatient follow-up is difficult (eg, those without health insurance).

A complete history and physical examination, along with ECG and cardiac biomarker testing, are required for all patients presenting with chest pain. Validated clinical risk prediction models should then be used to determine the likelihood of a cardiac event. Fanaroff et al. reported that low-risk HEART scores of 0–3 and TIMI scores of 0-1 gave positive likelihood ratios of 0.2 and 0.31, respectively.²² Using a pre-test probability of 13%, as reported by Bhuiya et al.,² the likelihood of ACS or MACE within 6 weeks is 2.9% for patients with low-risk HEART scores and 4.4% for those with low-risk TIMI scores.²² These risk prediction models allow clinicians to provide a shared decision-making plan with the patient and discuss the risks and benefits of in-hospital versus outpatient cardiac testing, especially among patients with access to appropriate outpatient follow-up.²³ Low-risk patients can be referred for outpatient testing within 72 h, reducing hospitalization-associated costs and harms.

• Patients presenting with chest pain should undergo a complete history taking and physical examination, as well as ECG and cardiac biomarker testing (eg, troponin I level at presentation and approximately 3 h later).
• Clinical risk prediction models, such as TIMI or HEART scores, should then be used to determine the risk of MACE.
• Patients at a low risk may be safely discharged with outpatient CST performed within 72 h.
• Patients at an intermediate or high risk of MACE should be hospitalized for further evaluation, as should those with low-risk chest pain who are unable to attend follow-up for outpatient CST within 72 h.
• Clinicians should provide a shared decision-making plan with each patient, taking care to discuss the risks and benefits of in-hospital versus outpatient CST.

The risk of MACE should be assessed in all patients presenting to ED with low-risk chest pain to avoid unnecessary hospitalization that exposes them to potential costs and harms with few additional benefits. If the risk scoring system was applied to the patient described in our original clinical scenario, he would have had a HEART score of 3 (ie, 1 point for a moderately suspicious history, 1 point for the age of 60 years, and 1 point for a positive family history) and a TIMI score of 1 (ie, 1 point for aspirin use within past 7 days). Therefore, he could be stratified as having a low-risk presentation. With a second negative troponin I test at 3 h, discharge from ED with timely outpatient CST within 72 h would be an appropriate management strategy.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason”? Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason” topics by emailing [email protected].

Conflicts of Interest

 The authors have no conflicts of interest relevant to this article to disclose.

The “Things We Do for No Reason” (TWDFNR) series reviews practices that have become common parts of hospital care but may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards, but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/

Chest pain is one of the most common complaints among patients presenting to the emergency department. Moreover, at least 30% of patients who present with chest pain are admitted for observation, and >70% of those admitted with chest pain undergo cardiac stress testing (CST) during hospitalization. Several clinical risk prediction models have validated evaluation processes for managing patients with chest pain, helping to identify those at a low risk of major adverse cardiac events. Among these, the Thrombolysis in Myocardial Infarction or HEART score can identify patients safe to be discharged with outpatient CST within 72 h. It is unnecessary to hospitalize all low-risk patients for cardiac testing because it may expose them to needless risk and avoidable care costs, with little additional benefit.

A 60-year-old man with a history of osteoarthritis and depression presented to our emergency department (ED) with a 1-month history of left-sided chest pain that was present both at rest and exertion. There were no aggravating or relieving factors for the pain and no associated shortness of breath, diaphoresis, nausea, or lightheadedness. He smoked a half pack of cigarettes daily for 5 years in his twenties. The patient was taking aspirin 81 mg daily and paroxetine 40 mg daily, which he had been taking for 10 years. There was a family history of coronary artery disease in his mother, father, and sister. On examination, he was afebrile, with a blood pressure of 138/78 mm Hg and a heart rate of 62 beats/min; he appeared well, with no abnormal cardiopulmonary findings. Investigation revealed a normal initial troponin I level (<0.034 mg/mL) and normal electrocardiogram (ECG) with normal sinus rhythm (75 beats/min), normal axis, no ST changes, and no Q waves. He was therefore admitted to the hospital for further evaluation.

Each year, >7 million patients visit ED for chest pain in the United States,¹ with approximately 13% diagnosed with acute coronary syndromes (ACSs).² Over 30% of patients who present to ED with chest pain are hospitalized for observation, symptom evaluation, and risk stratification.³ In 2012, the mean Medicare reimbursement cost was $1,741 for in-hospital observation,⁴ with up to 70% of admitted patients undergoing cardiac stress testing (CST) before discharge.⁵

A scientific statement by the American Heart Association in 2010 recommended that patients considered to be at low risk for ACS after initial evaluation (based on presenting symptoms, past history, ECG findings, and initial cardiac biomarkers) should undergo CST within 72 h (preferably within 24 h) of presentation to provoke ischemia or detect anatomic coronary artery disease.⁶ Early exercise treadmill testing as part of an accelerated diagnostic pathway can also reduce the length of stays (LOS) in hospital and lower the medical costs.⁷ Moreover, when there is noncompliance or poor accessibility, failure to pursue early exercise testing in a hospital could result in a loss of patients to follow-up. Hospitalization for testing through accelerated diagnostic pathways may improve access to care and reduce clinical and legal risks associated with a major adverse cardiac event (MACE).

Clinical Risk Prediction Models

When a patient initially presents with chest pain, it should be determined if the symptoms are related to ACS or some other diagnosis. Hospitalization is required for patients with ACS but may not be for those without ACS and those with a low risk of inducible ischemia. Clinical risk scores and risk prediction models, such as the Thrombolysis in Myocardial Infarction (TIMI) and HEART scores, have been used in accelerated diagnostic protocols to determine a patient’s likelihood of having ACS. Several large trials of these clinical risk prediction models have validated the processes for evaluating patients with chest pain.

The TIMI risk score, the most well-known model, assesses risk based on the presence or absence of 7 characteristics (Appendix 1). It should be noted that the patient population studied for initial validation of this model comprised high-risk patients with unstable angina or non-ST elevation myocardial infarction who would benefit from early or urgent invasive therapy.⁸ In this population, TIMI scores of 0-1 are associated with low risk, with a 4.7% risk of ACS at 14 days.⁸ In another study of patients presenting to ED with undifferentiated chest pain and a TIMI score of zero, the risk of MACE at 30 days was approximately 2%.⁹

The HEART score is also used for patients presenting to ED with undifferentiated chest pain and assesses 5 separate variables scored 0–2 (Appendix 2). The original research gave a score of 2 to a troponin I level greater than twice the upper limit of the normal level,¹⁰ whereas a subsequent validation study gave a score of 2 to a troponin I or T level greater than or equal to 3 times the upper limit of the normal level.¹¹ Patients are considered at low, intermediate, and high risk based on scores of 0–3, 4–6, and 7–10, respectively.^10,11 Backus et al. performed a prospective randomized trial of 2388 patients who presented to ED with chest pain to validate the HEART score and compare it to the TIMI risk score. The HEART score performed better than the TIMI risk score in low-risk patients, with TIMI scores of 0-1 and HEART scores of 0–3 having a 6-week MACE risk of 2.8% and 1.7%, respectively.¹¹

A HEART pathway was developed that combines the HEART score with serial troponin I assays assessed at the time of initial presentation and approximately 3 h later.¹² Mahler et al. randomized 282 patients presenting to ED with chest pain to either the HEART pathway or conventional care. Patients with low-risk HEART scores and an abnormal troponin I level were admitted for cardiology consultation, whereas discharge was recommended for those with low scores and a normal troponin I level. Despite nearly 20% of the study cohort having a history of myocardial infarction, percutaneous coronary intervention, or coronary artery bypass grafting, approximately 40% of patients in the HEART pathway were identified as low risk, increasing early discharge rates by 21.3% and decreasing the average LOS by 12 h. No low-risk patient suffered a MACE within 30 days, and the HEART pathway had a sensitivity and a negative predictive value of approximately 99%.

Costs and Harms of Hospitalization for Cardiac Testing

Hospitalization carries measurable risks.^13,14 Between 2008 and 2013, Weinstock et al. evaluated the outcomes of patients presenting with chest pain who were placed in an observation unit for suspected ACS.¹⁵ Low-risk patients were defined as those with normal ECGs (no ischemic changes), 2 negative troponin tests performed 60–420 min apart (no particular troponin assay specified), and stable vital signs. They identified 7266 patients who were considered to have low risk, among whom 4 (0.06%) had an adverse outcome in the hospital (eg, life-threatening arrhythmia, ST-segment elevation myocardial infarction, cardiac or respiratory arrest, or death); 3 among the 4 patients had a cardiac-related adverse outcome. The overall risk of adverse outcomes due to cardiac causes was 1 in 2422 admissions (0.04%). The authors compared their results with the reported risk of 1 in 164 admissions for preventable adverse events contributing to patient death during routine hospitalization (eg, medication or procedure errors).¹⁴

Outpatient CST can be reliably and safely performed for patients with chest pain.^16-18 There is no clear evidence that earlier CST leads to improved patient outcomes, and CST in the absence of acute ischemia (or ACS) increases the rates of angiography and revascularization without improvements in the rate of myocardial infarction.^19-21 Given the costs of in-hospital observation⁴ and the dubious benefits of providing CST for patients with low-risk chest pain, admitting all patients with low-risk chest pain exposes them to costs and harms with little potential benefit.

Patients presenting with chest pain with either dynamic ECG changes or an elevated troponin level require hospitalization for further ACS diagnosis and treatment. When ACS cannot be clearly diagnosed at the initial evaluation, healthcare providers should use clinical risk prediction models to stratify patients. Those deemed to be at an intermediate or high risk by these models should be hospitalized for further evaluation, as should those at low risk but for whom access to outpatient follow-up is difficult (eg, those without health insurance).

A complete history and physical examination, along with ECG and cardiac biomarker testing, are required for all patients presenting with chest pain. Validated clinical risk prediction models should then be used to determine the likelihood of a cardiac event. Fanaroff et al. reported that low-risk HEART scores of 0–3 and TIMI scores of 0-1 gave positive likelihood ratios of 0.2 and 0.31, respectively.²² Using a pre-test probability of 13%, as reported by Bhuiya et al.,² the likelihood of ACS or MACE within 6 weeks is 2.9% for patients with low-risk HEART scores and 4.4% for those with low-risk TIMI scores.²² These risk prediction models allow clinicians to provide a shared decision-making plan with the patient and discuss the risks and benefits of in-hospital versus outpatient cardiac testing, especially among patients with access to appropriate outpatient follow-up.²³ Low-risk patients can be referred for outpatient testing within 72 h, reducing hospitalization-associated costs and harms.

• Patients presenting with chest pain should undergo a complete history taking and physical examination, as well as ECG and cardiac biomarker testing (eg, troponin I level at presentation and approximately 3 h later).
• Clinical risk prediction models, such as TIMI or HEART scores, should then be used to determine the risk of MACE.
• Patients at a low risk may be safely discharged with outpatient CST performed within 72 h.
• Patients at an intermediate or high risk of MACE should be hospitalized for further evaluation, as should those with low-risk chest pain who are unable to attend follow-up for outpatient CST within 72 h.
• Clinicians should provide a shared decision-making plan with each patient, taking care to discuss the risks and benefits of in-hospital versus outpatient CST.

The risk of MACE should be assessed in all patients presenting to ED with low-risk chest pain to avoid unnecessary hospitalization that exposes them to potential costs and harms with few additional benefits. If the risk scoring system was applied to the patient described in our original clinical scenario, he would have had a HEART score of 3 (ie, 1 point for a moderately suspicious history, 1 point for the age of 60 years, and 1 point for a positive family history) and a TIMI score of 1 (ie, 1 point for aspirin use within past 7 days). Therefore, he could be stratified as having a low-risk presentation. With a second negative troponin I test at 3 h, discharge from ED with timely outpatient CST within 72 h would be an appropriate management strategy.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason”? Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason” topics by emailing [email protected].

Conflicts of Interest

 The authors have no conflicts of interest relevant to this article to disclose.

The “Things We Do for No Reason” (TWDFNR) series reviews practices that have become common parts of hospital care but may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards, but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/

Chest pain is one of the most common complaints among patients presenting to the emergency department. Moreover, at least 30% of patients who present with chest pain are admitted for observation, and >70% of those admitted with chest pain undergo cardiac stress testing (CST) during hospitalization. Several clinical risk prediction models have validated evaluation processes for managing patients with chest pain, helping to identify those at a low risk of major adverse cardiac events. Among these, the Thrombolysis in Myocardial Infarction or HEART score can identify patients safe to be discharged with outpatient CST within 72 h. It is unnecessary to hospitalize all low-risk patients for cardiac testing because it may expose them to needless risk and avoidable care costs, with little additional benefit.

A 60-year-old man with a history of osteoarthritis and depression presented to our emergency department (ED) with a 1-month history of left-sided chest pain that was present both at rest and exertion. There were no aggravating or relieving factors for the pain and no associated shortness of breath, diaphoresis, nausea, or lightheadedness. He smoked a half pack of cigarettes daily for 5 years in his twenties. The patient was taking aspirin 81 mg daily and paroxetine 40 mg daily, which he had been taking for 10 years. There was a family history of coronary artery disease in his mother, father, and sister. On examination, he was afebrile, with a blood pressure of 138/78 mm Hg and a heart rate of 62 beats/min; he appeared well, with no abnormal cardiopulmonary findings. Investigation revealed a normal initial troponin I level (<0.034 mg/mL) and normal electrocardiogram (ECG) with normal sinus rhythm (75 beats/min), normal axis, no ST changes, and no Q waves. He was therefore admitted to the hospital for further evaluation.

Each year, >7 million patients visit ED for chest pain in the United States,¹ with approximately 13% diagnosed with acute coronary syndromes (ACSs).² Over 30% of patients who present to ED with chest pain are hospitalized for observation, symptom evaluation, and risk stratification.³ In 2012, the mean Medicare reimbursement cost was $1,741 for in-hospital observation,⁴ with up to 70% of admitted patients undergoing cardiac stress testing (CST) before discharge.⁵

A scientific statement by the American Heart Association in 2010 recommended that patients considered to be at low risk for ACS after initial evaluation (based on presenting symptoms, past history, ECG findings, and initial cardiac biomarkers) should undergo CST within 72 h (preferably within 24 h) of presentation to provoke ischemia or detect anatomic coronary artery disease.⁶ Early exercise treadmill testing as part of an accelerated diagnostic pathway can also reduce the length of stays (LOS) in hospital and lower the medical costs.⁷ Moreover, when there is noncompliance or poor accessibility, failure to pursue early exercise testing in a hospital could result in a loss of patients to follow-up. Hospitalization for testing through accelerated diagnostic pathways may improve access to care and reduce clinical and legal risks associated with a major adverse cardiac event (MACE).

Clinical Risk Prediction Models

When a patient initially presents with chest pain, it should be determined if the symptoms are related to ACS or some other diagnosis. Hospitalization is required for patients with ACS but may not be for those without ACS and those with a low risk of inducible ischemia. Clinical risk scores and risk prediction models, such as the Thrombolysis in Myocardial Infarction (TIMI) and HEART scores, have been used in accelerated diagnostic protocols to determine a patient’s likelihood of having ACS. Several large trials of these clinical risk prediction models have validated the processes for evaluating patients with chest pain.

The TIMI risk score, the most well-known model, assesses risk based on the presence or absence of 7 characteristics (Appendix 1). It should be noted that the patient population studied for initial validation of this model comprised high-risk patients with unstable angina or non-ST elevation myocardial infarction who would benefit from early or urgent invasive therapy.⁸ In this population, TIMI scores of 0-1 are associated with low risk, with a 4.7% risk of ACS at 14 days.⁸ In another study of patients presenting to ED with undifferentiated chest pain and a TIMI score of zero, the risk of MACE at 30 days was approximately 2%.⁹

The HEART score is also used for patients presenting to ED with undifferentiated chest pain and assesses 5 separate variables scored 0–2 (Appendix 2). The original research gave a score of 2 to a troponin I level greater than twice the upper limit of the normal level,¹⁰ whereas a subsequent validation study gave a score of 2 to a troponin I or T level greater than or equal to 3 times the upper limit of the normal level.¹¹ Patients are considered at low, intermediate, and high risk based on scores of 0–3, 4–6, and 7–10, respectively.^10,11 Backus et al. performed a prospective randomized trial of 2388 patients who presented to ED with chest pain to validate the HEART score and compare it to the TIMI risk score. The HEART score performed better than the TIMI risk score in low-risk patients, with TIMI scores of 0-1 and HEART scores of 0–3 having a 6-week MACE risk of 2.8% and 1.7%, respectively.¹¹

A HEART pathway was developed that combines the HEART score with serial troponin I assays assessed at the time of initial presentation and approximately 3 h later.¹² Mahler et al. randomized 282 patients presenting to ED with chest pain to either the HEART pathway or conventional care. Patients with low-risk HEART scores and an abnormal troponin I level were admitted for cardiology consultation, whereas discharge was recommended for those with low scores and a normal troponin I level. Despite nearly 20% of the study cohort having a history of myocardial infarction, percutaneous coronary intervention, or coronary artery bypass grafting, approximately 40% of patients in the HEART pathway were identified as low risk, increasing early discharge rates by 21.3% and decreasing the average LOS by 12 h. No low-risk patient suffered a MACE within 30 days, and the HEART pathway had a sensitivity and a negative predictive value of approximately 99%.

Costs and Harms of Hospitalization for Cardiac Testing

Hospitalization carries measurable risks.^13,14 Between 2008 and 2013, Weinstock et al. evaluated the outcomes of patients presenting with chest pain who were placed in an observation unit for suspected ACS.¹⁵ Low-risk patients were defined as those with normal ECGs (no ischemic changes), 2 negative troponin tests performed 60–420 min apart (no particular troponin assay specified), and stable vital signs. They identified 7266 patients who were considered to have low risk, among whom 4 (0.06%) had an adverse outcome in the hospital (eg, life-threatening arrhythmia, ST-segment elevation myocardial infarction, cardiac or respiratory arrest, or death); 3 among the 4 patients had a cardiac-related adverse outcome. The overall risk of adverse outcomes due to cardiac causes was 1 in 2422 admissions (0.04%). The authors compared their results with the reported risk of 1 in 164 admissions for preventable adverse events contributing to patient death during routine hospitalization (eg, medication or procedure errors).¹⁴

Outpatient CST can be reliably and safely performed for patients with chest pain.^16-18 There is no clear evidence that earlier CST leads to improved patient outcomes, and CST in the absence of acute ischemia (or ACS) increases the rates of angiography and revascularization without improvements in the rate of myocardial infarction.^19-21 Given the costs of in-hospital observation⁴ and the dubious benefits of providing CST for patients with low-risk chest pain, admitting all patients with low-risk chest pain exposes them to costs and harms with little potential benefit.

Patients presenting with chest pain with either dynamic ECG changes or an elevated troponin level require hospitalization for further ACS diagnosis and treatment. When ACS cannot be clearly diagnosed at the initial evaluation, healthcare providers should use clinical risk prediction models to stratify patients. Those deemed to be at an intermediate or high risk by these models should be hospitalized for further evaluation, as should those at low risk but for whom access to outpatient follow-up is difficult (eg, those without health insurance).

A complete history and physical examination, along with ECG and cardiac biomarker testing, are required for all patients presenting with chest pain. Validated clinical risk prediction models should then be used to determine the likelihood of a cardiac event. Fanaroff et al. reported that low-risk HEART scores of 0–3 and TIMI scores of 0-1 gave positive likelihood ratios of 0.2 and 0.31, respectively.²² Using a pre-test probability of 13%, as reported by Bhuiya et al.,² the likelihood of ACS or MACE within 6 weeks is 2.9% for patients with low-risk HEART scores and 4.4% for those with low-risk TIMI scores.²² These risk prediction models allow clinicians to provide a shared decision-making plan with the patient and discuss the risks and benefits of in-hospital versus outpatient cardiac testing, especially among patients with access to appropriate outpatient follow-up.²³ Low-risk patients can be referred for outpatient testing within 72 h, reducing hospitalization-associated costs and harms.

• Patients presenting with chest pain should undergo a complete history taking and physical examination, as well as ECG and cardiac biomarker testing (eg, troponin I level at presentation and approximately 3 h later).
• Clinical risk prediction models, such as TIMI or HEART scores, should then be used to determine the risk of MACE.
• Patients at a low risk may be safely discharged with outpatient CST performed within 72 h.
• Patients at an intermediate or high risk of MACE should be hospitalized for further evaluation, as should those with low-risk chest pain who are unable to attend follow-up for outpatient CST within 72 h.
• Clinicians should provide a shared decision-making plan with each patient, taking care to discuss the risks and benefits of in-hospital versus outpatient CST.

The risk of MACE should be assessed in all patients presenting to ED with low-risk chest pain to avoid unnecessary hospitalization that exposes them to potential costs and harms with few additional benefits. If the risk scoring system was applied to the patient described in our original clinical scenario, he would have had a HEART score of 3 (ie, 1 point for a moderately suspicious history, 1 point for the age of 60 years, and 1 point for a positive family history) and a TIMI score of 1 (ie, 1 point for aspirin use within past 7 days). Therefore, he could be stratified as having a low-risk presentation. With a second negative troponin I test at 3 h, discharge from ED with timely outpatient CST within 72 h would be an appropriate management strategy.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason”? Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason” topics by emailing [email protected].

Conflicts of Interest

 The authors have no conflicts of interest relevant to this article to disclose.

Most studies to identify risk factors for readmission among patients with heart failure (HF) have focused on demographic and clinical characteristics.^1,2 Although easy to extract from administrative databases, this approach fails to capture the complex psychosocial and cognitive factors that influence the ability of HF patients to manage their disease in the postdischarge period, as depicted in the framework by Meyers et al.³ (2014). To date, studies have found low health literacy, decreased social support, and cognitive impairment to be associated with health behaviors and outcomes among HF patients, including decreased self-care,⁴ low HF-specific knowledge,⁵ medication nonadherence,⁶ hospitalizations,⁷ and mortality.^8-10 Less, however, is known about the effect of numeracy on HF outcomes, such as 30-day readmission.

Numeracy, or quantitative literacy, refers to the ability to access, understand, and apply numerical data to health-related decisions.¹¹ It is estimated that 110 million people in the United States have limited numeracy skills.¹² Low numeracy is a risk factor for poor glycemic control among patients with diabetes,¹³ medication adherence in HIV/AIDS,¹⁴ and worse blood pressure control in hypertensives.¹⁵ Much like these conditions, HF requires that patients understand, use, and act on numerical information. Maintaining a low-salt diet, monitoring weight, adjusting diuretic doses, and measuring blood pressure are tasks that HF patients are asked to perform on a daily or near-daily basis. These tasks are particularly important in the posthospitalization period and could be complicated by medication changes, which might create additional challenges for patients with inadequate numeracy. Additionally, cognitive impairment, which is a highly prevalent comorbid condition among adults with HF,^16,17 might impose additional barriers for those with inadequate numeracy who do not have adequate social support. However, to date, numeracy in the context of HF has not been well described.

Herein, we examined the effects of numeracy, alongside health literacy and cognition, on 30-day readmission risk among patients hospitalized for acute decompensated HF (ADHF).

Study Design

The Vanderbilt Inpatient Cohort Study (VICS) is a prospective observational study of patients admitted with cardiovascular disease to Vanderbilt University Medical Center (VUMC), an academic tertiary care hospital. VICS was designed to investigate the impact of social determinants of health on postdischarge health outcomes. A detailed description of the study rationale, design, and methods is described elsewhere.³

Briefly, participants completed a baseline interview while hospitalized, and follow-up phone calls were conducted within 1 week of discharge, at 30 days, and at 90 days. At 30 and 90 days postdischarge, healthcare utilization was ascertained by review of medical records and patient report. Clinical data about the index hospitalization were also abstracted. The Vanderbilt University Institutional Review Board approved the study.

Study Population

Patients hospitalized from 2011 to 2015 with a likely diagnosis of acute coronary syndrome and/or ADHF, as determined by a physician’s review of the medical record, were identified as potentially eligible. Research assistants assessed these patients for the presence of the following exclusion criteria: less than 18 years of age, non-English speaking, unstable psychiatric illness, a low likelihood of follow-up (eg, no reliable telephone number), on hospice, or otherwise too ill to complete an interview. Additionally, those with severe cognitive impairment, as assessed from the medical record (such as seeing a note describing dementia), and those with delirium, as assessed by the brief confusion assessment method, were excluded from enrollment in the study.^18,19 Those who died before discharge or during the 30-day follow-up period were excluded. For this analysis, we restricted our sample to only include participants who were hospitalized for ADHF.

Outcome Measure: 30-Day Readmission

The main outcome was all-cause readmission to any hospital within 30 days of discharge, as determined by patient interview, review of electronic medical records from VUMC, and review of outside hospital records.

Main Exposures: Numeracy, Health Literacy, and Cognitive Impairment

Numeracy was assessed with a 3-item version of the Subjective Numeracy Scale (SNS-3), which quantifies the patients perceived quantitative abilities.²⁰ Other authors have shown that the SNS-3 has a correlation coefficient of 0.88 with the full-length SNS-8 and a Cronbach’s alpha of 0.78.^20-22 The SNS-3 is reported as the mean on a scale from 1 to 6, with higher scores reflecting higher numeracy.

Subjective health literacy was assessed by using the 3-item Brief Health Literacy Screen (BHLS).²³ Scores range from 3 to 15, with higher scores reflecting higher literacy. Objective health literacy was assessed with the short form of the Test of Functional Health Literacy in Adults (sTOFHLA).^24,25 Scores may be categorized as inadequate (0-16), marginal (17-22), or adequate (23-36).

We assessed cognition by using the 10-item Short Portable Mental Status Questionnaire (SPMSQ).²⁶ The SPMSQ, which describes a person’s capacity for memory, structured thought, and orientation, has been validated and has demonstrated good reliability and validity.²⁷ Scores of 0 were considered to reflect intact cognition, and scores of 1 or more were considered to reflect any cognitive impairment, a scoring approach employed by other authors.²⁸ We used this approach, rather than the traditional scoring system developed by Pfeiffer et al.²⁶ (1975), because it would be the most sensitive to detect any cognitive impairment in the VICS cohort, which excluded those with severe cognition impairment, dementia, and delirium.

Covariates

During the hospitalization, participants completed an in-person interviewer-administered baseline assessment composed of demographic information, including age, self-reported race (white and nonwhite), educational attainment, home status (married, not married and living with someone, not married and living alone), and household income.

Clinical and diagnostic characteristics abstracted from the medical record included a medical history of HF, HF subtype (classified by left ventricular ejection fraction [LVEF]), coronary artery disease, chronic obstructive pulmonary disease (COPD), diabetes mellitus (DM), and comorbidity burden as summarized by the van Walraven-Elixhauser score.^29,30 Depressive symptoms were assessed during the 2 weeks prior to the hospitalization by using the first 8 items of the Patient Health Questionnaire.³¹ Scores ranged from 0 to 24, with higher scores reflecting more severe depressive symptoms. Laboratory values included estimated glomerular filtration rate (eGFR), hemoglobin (g/dl), sodium (mg/L), and brain natriuretic peptide (BNP) (pg/ml) from the last laboratory draw before discharge. Smoking status was also assessed (current and former/nonsmokers).

Hospitalization characteristics included length of stay in days, number of prior admissions in the last year, and transfer to the intensive care unit during the index admission.

Statistical Analysis

Descriptive statistics were used to summarize patient characteristics. The Kruskal-Wallis test and the Pearson χ² test were used to determine the association between patient characteristics and levels of numeracy, literacy, and cognition separately. The unadjusted relationship between patient characteristics and 30-day readmission was assessed by using Wilcoxon rank sums tests for continuous variables and Pearson χ² tests for categorical variables. In addition, a correlation matrix was performed to assess the correlations between numeracy, health literacy, and cognition (supplementary Figure 1).

To examine the association between numeracy, health literacy, and cognition and 30-day readmissions, a series of multivariable Poisson (log-linear) regression models were fit.³² Like other studies, numeracy, health literacy, and cognition were examined as categorical and continuous measures in models.³³ Each model was modified with a sandwich estimator for robust standard errors. Log-linear models were chosen over logistic regression models for ease of interpretation because (exponentiated) parameters correspond to risk ratios (RRs) as opposed to odds ratios. Furthermore, the fitting challenges associated with log-linear models when predicted probabilities are near 0 or 1 were not present in these analyses. Redundancy analyses were conducted to ensure that independent variables were not highly correlated with a linear combination of the other independent variables. To avoid case-wise deletion of records with missing covariates, we employed multiple imputation with 10 imputation samples by using predictive mean matching.^34,35 All analyses were conducted in R version 3.1.2 (The R Foundation, Vienna, Austria).³⁶

Overall, 883 patients were included in this analysis (supplementary Figure 2). Of the 883 participants, 46% were female and 76% were white (Table 1). Their median age was 60 years (interdecile range [IDR] 39-78) and the median educational attainment was 13.5 years (IDR 11-18).

This is the first study to examine the effect of numeracy alongside literacy and cognition on 30-day readmission risk among patients hospitalized with ADHF. Overall, we found that 33.9% of participants had inadequate numeracy skills, and 24.6% had inadequate or marginal health literacy. In unadjusted and adjusted models, numeracy was not associated with 30-day readmission. Although (objective) low health literacy was associated with 30-day readmission in unadjusted models, it was not in adjusted models. Additionally, though 53% of participants had any cognitive impairment, readmission did not differ significantly by this factor. Taken together, these findings suggest that other factors may be greater determinants of 30-day readmissions among patients hospitalized for ADHF.

Only 1 other study has examined the effect of numeracy on readmission risk among patients hospitalized for HF. In this multicenter prospective study, McNaughton et al.³⁷ found low numeracy to be associated with higher odds of recidivism to the emergency department (ED) or hospital within 30 days. Our findings may differ from theirs for a few reasons. First, their study had a significantly higher percentage of individuals with low numeracy (55%) compared with ours (33.9%). This may be because they did not exclude individuals with severe cognitive impairment, and their patient population was of lower socioeconomic status (SES) than ours. Low SES is associated with higher 30-day readmissions among HF patients^1,10 throughout the literature, and low numeracy is associated with low SES in other diseases.^13,38,39 Finally, they studied recidivism, which was defined as any unplanned return to the ED or hospital within 30 days of the index ED visit for acute HF. We only focused on 30-day readmissions, which also may explain why our results differed.

We found that health literacy was not associated with 30-day readmissions, which is consistent with the literature. Although an association between health literacy and mortality exists among adults with HF, several studies have not found an association between health literacy and 30- and 90-day readmission among adults hospitalized for HF.^8,9,40 Although we found an association between objective health literacy and 30-day readmission in unadjusted analyses, we did not find one in the multivariable model. This, along with our numeracy finding, suggests that numeracy and literacy may not be driving the 30-day readmission risk among patients hospitalized with ADHF.

We examined cognition alongside numeracy and literacy because it is a prevalent condition among HF patients and because it is associated with adverse outcomes among patients with HF, including readmission.^41,42 Studies have shown that HF preferentially affects certain cognitive domains,⁴³ some of which are vital to HF self-care activities. We found that 53% of patients had any cognitive impairment, which is consistent with the literature of adults hospitalized for ADHF.^44,45 Cognitive impairment was not, however, associated with 30-day readmissions. There may be a couple reasons for this. First, we measured cognitive impairment with the SPMSQ, which, although widely used and well-validated, does not assess executive function, the domain most commonly affected in HF patients with cognitive impairment.⁴⁶ Second, patients with severe cognitive impairment and those with delirium were excluded from this study, which may have limited our ability to detect differences in readmission by this factor.

As in prior studies, we found that a history of DM and more hospitalizations in the prior year were independently associated with 30-day readmissions in fully adjusted models. Like other studies, in adjusted models, we found that LVEF and a history of HF were not independently associated with 30-day readmission.^47-49 This, however, is not surprising because recent studies have shown that, although HF patients are at risk for multiple hospitalizations, early readmission after a hospitalization for ADHF specifically is often because of reasons unrelated to HF or a non-cardiovascular cause in general.^50,51

Although a negative study, several important themes emerged. First, while we were able to assess numeracy, health literacy, and cognition, none of these measures were HF-specific. It is possible that we did not see an effect on readmission because our instruments failed to assess domains specific to HF, such as monitoring weight changes, following a low-salt diet, and interpreting blood pressure. Currently, however, no HF-specific objective numeracy measure exists. With respect to health literacy, only 1 HF-specific measure exists,⁵² although it was only recently developed and validated. Second, while numeracy may not be a driving influence of all-cause 30-day readmissions, it may be associated with other health behaviors and quality metrics that we did not examine here, such as self-care, medication adherence, and HF-specific readmissions. Third, it is likely that the progression of HF itself, as well as the clinical management of patients following discharge, contribute significantly to 30-day readmissions. Increased attention to predischarge processes for HF patients occurred at VUMC during the study period; close follow-up and evidence-directed therapies may have mitigated some of the expected associations. Finally, we were not able to assess numeracy of participants’ primary caregivers who may help patients at home, especially postdischarge. Though a number of studies have examined the role of family caregivers in the management of HF,^53,54 none have examined numeracy levels of caregivers in the context of HF, and this may be worth doing in future studies.

Overall, our study has several strengths. The size of the cohort is large and there were high response rates during the follow-up period. Unlike other HF readmission studies, VICS accounts for readmissions to outside hospitals. Approximately 35% of all hospitalizations in VICS are to outside facilities. Thus, the ascertainment of readmissions to hospitals other than Vanderbilt is more comprehensive than if readmissions to VUMC were only considered. We were able to include a number of clinical comorbidities, laboratory and diagnostic tests from the index admission, and hospitalization characteristics in our analyses. Finally, we performed additional analyses to investigate the correlation between numeracy, literacy, and cognition; ultimately, we found that the majority of these correlations were weak, which supports our ability to study them simultaneously among VICS participants.

Nonetheless, we note some limitations. Although we captured readmissions to outside hospitals, the study took place at a single referral center in Tennessee. Though patients were diverse in age and comorbidities, they were mostly white and of higher SES. Finally, we used home status as a proxy for social support, which may underestimate the support that home care workers provide.

In conclusion, in this prospective longitudinal study of adults hospitalized with ADHF, inadequate numeracy was present in more than a third of patients, and low health literacy was present in roughly a quarter of patients. Neither numeracy nor health literacy, however, were associated with 30-day readmissions in adjusted analyses. Any cognitive impairment, although present in roughly one-half of patients, was not associated with 30-day readmission either. Our findings suggest that other influences may play a more dominant role in determining 30-day readmission rates in patients hospitalized for ADHF than inadequate numeracy, low health literacy, or cognitive impairment as assessed here.

Acknowledgments

This research was supported by the National Heart, Lung, and Blood Institute (R01 HL109388) and in part by the National Center for Advancing Translational Sciences (UL1 TR000445-06). The content is solely the responsibility of the authors and does not necessarily represent official views of the National Institutes of Health. The authors’ funding sources did not participate in the planning, collection, analysis, or interpretation of data or in the decision to submit for publication. Dr. Sterling is supported by T32HS000066 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. Dr. Mixon has a VA Health Services Research and Development Service Career Development Award at the Tennessee Valley Healthcare System, Department of Veterans Affairs (CDA 12-168). This material was presented at the Society of General Internal Medicine Annual Meeting on April 20, 2017, in Washington, DC.

Disclosure

Dr. Kripalani reports personal fees from Verustat, personal fees from SAI Interactive, and equity from Bioscape Digital, all outside of the submitted work. Dr. Rothman and Dr. Wallston report personal fees from EdLogics outside of the submitted work. All of the other authors have nothing to disclose

Most studies to identify risk factors for readmission among patients with heart failure (HF) have focused on demographic and clinical characteristics.^1,2 Although easy to extract from administrative databases, this approach fails to capture the complex psychosocial and cognitive factors that influence the ability of HF patients to manage their disease in the postdischarge period, as depicted in the framework by Meyers et al.³ (2014). To date, studies have found low health literacy, decreased social support, and cognitive impairment to be associated with health behaviors and outcomes among HF patients, including decreased self-care,⁴ low HF-specific knowledge,⁵ medication nonadherence,⁶ hospitalizations,⁷ and mortality.^8-10 Less, however, is known about the effect of numeracy on HF outcomes, such as 30-day readmission.

Numeracy, or quantitative literacy, refers to the ability to access, understand, and apply numerical data to health-related decisions.¹¹ It is estimated that 110 million people in the United States have limited numeracy skills.¹² Low numeracy is a risk factor for poor glycemic control among patients with diabetes,¹³ medication adherence in HIV/AIDS,¹⁴ and worse blood pressure control in hypertensives.¹⁵ Much like these conditions, HF requires that patients understand, use, and act on numerical information. Maintaining a low-salt diet, monitoring weight, adjusting diuretic doses, and measuring blood pressure are tasks that HF patients are asked to perform on a daily or near-daily basis. These tasks are particularly important in the posthospitalization period and could be complicated by medication changes, which might create additional challenges for patients with inadequate numeracy. Additionally, cognitive impairment, which is a highly prevalent comorbid condition among adults with HF,^16,17 might impose additional barriers for those with inadequate numeracy who do not have adequate social support. However, to date, numeracy in the context of HF has not been well described.

Herein, we examined the effects of numeracy, alongside health literacy and cognition, on 30-day readmission risk among patients hospitalized for acute decompensated HF (ADHF).

Study Design

The Vanderbilt Inpatient Cohort Study (VICS) is a prospective observational study of patients admitted with cardiovascular disease to Vanderbilt University Medical Center (VUMC), an academic tertiary care hospital. VICS was designed to investigate the impact of social determinants of health on postdischarge health outcomes. A detailed description of the study rationale, design, and methods is described elsewhere.³

Briefly, participants completed a baseline interview while hospitalized, and follow-up phone calls were conducted within 1 week of discharge, at 30 days, and at 90 days. At 30 and 90 days postdischarge, healthcare utilization was ascertained by review of medical records and patient report. Clinical data about the index hospitalization were also abstracted. The Vanderbilt University Institutional Review Board approved the study.

Study Population

Patients hospitalized from 2011 to 2015 with a likely diagnosis of acute coronary syndrome and/or ADHF, as determined by a physician’s review of the medical record, were identified as potentially eligible. Research assistants assessed these patients for the presence of the following exclusion criteria: less than 18 years of age, non-English speaking, unstable psychiatric illness, a low likelihood of follow-up (eg, no reliable telephone number), on hospice, or otherwise too ill to complete an interview. Additionally, those with severe cognitive impairment, as assessed from the medical record (such as seeing a note describing dementia), and those with delirium, as assessed by the brief confusion assessment method, were excluded from enrollment in the study.^18,19 Those who died before discharge or during the 30-day follow-up period were excluded. For this analysis, we restricted our sample to only include participants who were hospitalized for ADHF.

Outcome Measure: 30-Day Readmission

The main outcome was all-cause readmission to any hospital within 30 days of discharge, as determined by patient interview, review of electronic medical records from VUMC, and review of outside hospital records.

Main Exposures: Numeracy, Health Literacy, and Cognitive Impairment

Numeracy was assessed with a 3-item version of the Subjective Numeracy Scale (SNS-3), which quantifies the patients perceived quantitative abilities.²⁰ Other authors have shown that the SNS-3 has a correlation coefficient of 0.88 with the full-length SNS-8 and a Cronbach’s alpha of 0.78.^20-22 The SNS-3 is reported as the mean on a scale from 1 to 6, with higher scores reflecting higher numeracy.

Subjective health literacy was assessed by using the 3-item Brief Health Literacy Screen (BHLS).²³ Scores range from 3 to 15, with higher scores reflecting higher literacy. Objective health literacy was assessed with the short form of the Test of Functional Health Literacy in Adults (sTOFHLA).^24,25 Scores may be categorized as inadequate (0-16), marginal (17-22), or adequate (23-36).

We assessed cognition by using the 10-item Short Portable Mental Status Questionnaire (SPMSQ).²⁶ The SPMSQ, which describes a person’s capacity for memory, structured thought, and orientation, has been validated and has demonstrated good reliability and validity.²⁷ Scores of 0 were considered to reflect intact cognition, and scores of 1 or more were considered to reflect any cognitive impairment, a scoring approach employed by other authors.²⁸ We used this approach, rather than the traditional scoring system developed by Pfeiffer et al.²⁶ (1975), because it would be the most sensitive to detect any cognitive impairment in the VICS cohort, which excluded those with severe cognition impairment, dementia, and delirium.

Covariates

During the hospitalization, participants completed an in-person interviewer-administered baseline assessment composed of demographic information, including age, self-reported race (white and nonwhite), educational attainment, home status (married, not married and living with someone, not married and living alone), and household income.

Clinical and diagnostic characteristics abstracted from the medical record included a medical history of HF, HF subtype (classified by left ventricular ejection fraction [LVEF]), coronary artery disease, chronic obstructive pulmonary disease (COPD), diabetes mellitus (DM), and comorbidity burden as summarized by the van Walraven-Elixhauser score.^29,30 Depressive symptoms were assessed during the 2 weeks prior to the hospitalization by using the first 8 items of the Patient Health Questionnaire.³¹ Scores ranged from 0 to 24, with higher scores reflecting more severe depressive symptoms. Laboratory values included estimated glomerular filtration rate (eGFR), hemoglobin (g/dl), sodium (mg/L), and brain natriuretic peptide (BNP) (pg/ml) from the last laboratory draw before discharge. Smoking status was also assessed (current and former/nonsmokers).

Hospitalization characteristics included length of stay in days, number of prior admissions in the last year, and transfer to the intensive care unit during the index admission.

Statistical Analysis

Descriptive statistics were used to summarize patient characteristics. The Kruskal-Wallis test and the Pearson χ² test were used to determine the association between patient characteristics and levels of numeracy, literacy, and cognition separately. The unadjusted relationship between patient characteristics and 30-day readmission was assessed by using Wilcoxon rank sums tests for continuous variables and Pearson χ² tests for categorical variables. In addition, a correlation matrix was performed to assess the correlations between numeracy, health literacy, and cognition (supplementary Figure 1).

To examine the association between numeracy, health literacy, and cognition and 30-day readmissions, a series of multivariable Poisson (log-linear) regression models were fit.³² Like other studies, numeracy, health literacy, and cognition were examined as categorical and continuous measures in models.³³ Each model was modified with a sandwich estimator for robust standard errors. Log-linear models were chosen over logistic regression models for ease of interpretation because (exponentiated) parameters correspond to risk ratios (RRs) as opposed to odds ratios. Furthermore, the fitting challenges associated with log-linear models when predicted probabilities are near 0 or 1 were not present in these analyses. Redundancy analyses were conducted to ensure that independent variables were not highly correlated with a linear combination of the other independent variables. To avoid case-wise deletion of records with missing covariates, we employed multiple imputation with 10 imputation samples by using predictive mean matching.^34,35 All analyses were conducted in R version 3.1.2 (The R Foundation, Vienna, Austria).³⁶

Overall, 883 patients were included in this analysis (supplementary Figure 2). Of the 883 participants, 46% were female and 76% were white (Table 1). Their median age was 60 years (interdecile range [IDR] 39-78) and the median educational attainment was 13.5 years (IDR 11-18).

This is the first study to examine the effect of numeracy alongside literacy and cognition on 30-day readmission risk among patients hospitalized with ADHF. Overall, we found that 33.9% of participants had inadequate numeracy skills, and 24.6% had inadequate or marginal health literacy. In unadjusted and adjusted models, numeracy was not associated with 30-day readmission. Although (objective) low health literacy was associated with 30-day readmission in unadjusted models, it was not in adjusted models. Additionally, though 53% of participants had any cognitive impairment, readmission did not differ significantly by this factor. Taken together, these findings suggest that other factors may be greater determinants of 30-day readmissions among patients hospitalized for ADHF.

Only 1 other study has examined the effect of numeracy on readmission risk among patients hospitalized for HF. In this multicenter prospective study, McNaughton et al.³⁷ found low numeracy to be associated with higher odds of recidivism to the emergency department (ED) or hospital within 30 days. Our findings may differ from theirs for a few reasons. First, their study had a significantly higher percentage of individuals with low numeracy (55%) compared with ours (33.9%). This may be because they did not exclude individuals with severe cognitive impairment, and their patient population was of lower socioeconomic status (SES) than ours. Low SES is associated with higher 30-day readmissions among HF patients^1,10 throughout the literature, and low numeracy is associated with low SES in other diseases.^13,38,39 Finally, they studied recidivism, which was defined as any unplanned return to the ED or hospital within 30 days of the index ED visit for acute HF. We only focused on 30-day readmissions, which also may explain why our results differed.

We found that health literacy was not associated with 30-day readmissions, which is consistent with the literature. Although an association between health literacy and mortality exists among adults with HF, several studies have not found an association between health literacy and 30- and 90-day readmission among adults hospitalized for HF.^8,9,40 Although we found an association between objective health literacy and 30-day readmission in unadjusted analyses, we did not find one in the multivariable model. This, along with our numeracy finding, suggests that numeracy and literacy may not be driving the 30-day readmission risk among patients hospitalized with ADHF.

We examined cognition alongside numeracy and literacy because it is a prevalent condition among HF patients and because it is associated with adverse outcomes among patients with HF, including readmission.^41,42 Studies have shown that HF preferentially affects certain cognitive domains,⁴³ some of which are vital to HF self-care activities. We found that 53% of patients had any cognitive impairment, which is consistent with the literature of adults hospitalized for ADHF.^44,45 Cognitive impairment was not, however, associated with 30-day readmissions. There may be a couple reasons for this. First, we measured cognitive impairment with the SPMSQ, which, although widely used and well-validated, does not assess executive function, the domain most commonly affected in HF patients with cognitive impairment.⁴⁶ Second, patients with severe cognitive impairment and those with delirium were excluded from this study, which may have limited our ability to detect differences in readmission by this factor.

As in prior studies, we found that a history of DM and more hospitalizations in the prior year were independently associated with 30-day readmissions in fully adjusted models. Like other studies, in adjusted models, we found that LVEF and a history of HF were not independently associated with 30-day readmission.^47-49 This, however, is not surprising because recent studies have shown that, although HF patients are at risk for multiple hospitalizations, early readmission after a hospitalization for ADHF specifically is often because of reasons unrelated to HF or a non-cardiovascular cause in general.^50,51

Although a negative study, several important themes emerged. First, while we were able to assess numeracy, health literacy, and cognition, none of these measures were HF-specific. It is possible that we did not see an effect on readmission because our instruments failed to assess domains specific to HF, such as monitoring weight changes, following a low-salt diet, and interpreting blood pressure. Currently, however, no HF-specific objective numeracy measure exists. With respect to health literacy, only 1 HF-specific measure exists,⁵² although it was only recently developed and validated. Second, while numeracy may not be a driving influence of all-cause 30-day readmissions, it may be associated with other health behaviors and quality metrics that we did not examine here, such as self-care, medication adherence, and HF-specific readmissions. Third, it is likely that the progression of HF itself, as well as the clinical management of patients following discharge, contribute significantly to 30-day readmissions. Increased attention to predischarge processes for HF patients occurred at VUMC during the study period; close follow-up and evidence-directed therapies may have mitigated some of the expected associations. Finally, we were not able to assess numeracy of participants’ primary caregivers who may help patients at home, especially postdischarge. Though a number of studies have examined the role of family caregivers in the management of HF,^53,54 none have examined numeracy levels of caregivers in the context of HF, and this may be worth doing in future studies.

Overall, our study has several strengths. The size of the cohort is large and there were high response rates during the follow-up period. Unlike other HF readmission studies, VICS accounts for readmissions to outside hospitals. Approximately 35% of all hospitalizations in VICS are to outside facilities. Thus, the ascertainment of readmissions to hospitals other than Vanderbilt is more comprehensive than if readmissions to VUMC were only considered. We were able to include a number of clinical comorbidities, laboratory and diagnostic tests from the index admission, and hospitalization characteristics in our analyses. Finally, we performed additional analyses to investigate the correlation between numeracy, literacy, and cognition; ultimately, we found that the majority of these correlations were weak, which supports our ability to study them simultaneously among VICS participants.

Nonetheless, we note some limitations. Although we captured readmissions to outside hospitals, the study took place at a single referral center in Tennessee. Though patients were diverse in age and comorbidities, they were mostly white and of higher SES. Finally, we used home status as a proxy for social support, which may underestimate the support that home care workers provide.

In conclusion, in this prospective longitudinal study of adults hospitalized with ADHF, inadequate numeracy was present in more than a third of patients, and low health literacy was present in roughly a quarter of patients. Neither numeracy nor health literacy, however, were associated with 30-day readmissions in adjusted analyses. Any cognitive impairment, although present in roughly one-half of patients, was not associated with 30-day readmission either. Our findings suggest that other influences may play a more dominant role in determining 30-day readmission rates in patients hospitalized for ADHF than inadequate numeracy, low health literacy, or cognitive impairment as assessed here.

Acknowledgments

This research was supported by the National Heart, Lung, and Blood Institute (R01 HL109388) and in part by the National Center for Advancing Translational Sciences (UL1 TR000445-06). The content is solely the responsibility of the authors and does not necessarily represent official views of the National Institutes of Health. The authors’ funding sources did not participate in the planning, collection, analysis, or interpretation of data or in the decision to submit for publication. Dr. Sterling is supported by T32HS000066 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. Dr. Mixon has a VA Health Services Research and Development Service Career Development Award at the Tennessee Valley Healthcare System, Department of Veterans Affairs (CDA 12-168). This material was presented at the Society of General Internal Medicine Annual Meeting on April 20, 2017, in Washington, DC.

Disclosure

Dr. Kripalani reports personal fees from Verustat, personal fees from SAI Interactive, and equity from Bioscape Digital, all outside of the submitted work. Dr. Rothman and Dr. Wallston report personal fees from EdLogics outside of the submitted work. All of the other authors have nothing to disclose

Most studies to identify risk factors for readmission among patients with heart failure (HF) have focused on demographic and clinical characteristics.^1,2 Although easy to extract from administrative databases, this approach fails to capture the complex psychosocial and cognitive factors that influence the ability of HF patients to manage their disease in the postdischarge period, as depicted in the framework by Meyers et al.³ (2014). To date, studies have found low health literacy, decreased social support, and cognitive impairment to be associated with health behaviors and outcomes among HF patients, including decreased self-care,⁴ low HF-specific knowledge,⁵ medication nonadherence,⁶ hospitalizations,⁷ and mortality.^8-10 Less, however, is known about the effect of numeracy on HF outcomes, such as 30-day readmission.

Numeracy, or quantitative literacy, refers to the ability to access, understand, and apply numerical data to health-related decisions.¹¹ It is estimated that 110 million people in the United States have limited numeracy skills.¹² Low numeracy is a risk factor for poor glycemic control among patients with diabetes,¹³ medication adherence in HIV/AIDS,¹⁴ and worse blood pressure control in hypertensives.¹⁵ Much like these conditions, HF requires that patients understand, use, and act on numerical information. Maintaining a low-salt diet, monitoring weight, adjusting diuretic doses, and measuring blood pressure are tasks that HF patients are asked to perform on a daily or near-daily basis. These tasks are particularly important in the posthospitalization period and could be complicated by medication changes, which might create additional challenges for patients with inadequate numeracy. Additionally, cognitive impairment, which is a highly prevalent comorbid condition among adults with HF,^16,17 might impose additional barriers for those with inadequate numeracy who do not have adequate social support. However, to date, numeracy in the context of HF has not been well described.

Herein, we examined the effects of numeracy, alongside health literacy and cognition, on 30-day readmission risk among patients hospitalized for acute decompensated HF (ADHF).

Study Design

The Vanderbilt Inpatient Cohort Study (VICS) is a prospective observational study of patients admitted with cardiovascular disease to Vanderbilt University Medical Center (VUMC), an academic tertiary care hospital. VICS was designed to investigate the impact of social determinants of health on postdischarge health outcomes. A detailed description of the study rationale, design, and methods is described elsewhere.³

Briefly, participants completed a baseline interview while hospitalized, and follow-up phone calls were conducted within 1 week of discharge, at 30 days, and at 90 days. At 30 and 90 days postdischarge, healthcare utilization was ascertained by review of medical records and patient report. Clinical data about the index hospitalization were also abstracted. The Vanderbilt University Institutional Review Board approved the study.

Study Population

Patients hospitalized from 2011 to 2015 with a likely diagnosis of acute coronary syndrome and/or ADHF, as determined by a physician’s review of the medical record, were identified as potentially eligible. Research assistants assessed these patients for the presence of the following exclusion criteria: less than 18 years of age, non-English speaking, unstable psychiatric illness, a low likelihood of follow-up (eg, no reliable telephone number), on hospice, or otherwise too ill to complete an interview. Additionally, those with severe cognitive impairment, as assessed from the medical record (such as seeing a note describing dementia), and those with delirium, as assessed by the brief confusion assessment method, were excluded from enrollment in the study.^18,19 Those who died before discharge or during the 30-day follow-up period were excluded. For this analysis, we restricted our sample to only include participants who were hospitalized for ADHF.

Outcome Measure: 30-Day Readmission

The main outcome was all-cause readmission to any hospital within 30 days of discharge, as determined by patient interview, review of electronic medical records from VUMC, and review of outside hospital records.

Main Exposures: Numeracy, Health Literacy, and Cognitive Impairment

Numeracy was assessed with a 3-item version of the Subjective Numeracy Scale (SNS-3), which quantifies the patients perceived quantitative abilities.²⁰ Other authors have shown that the SNS-3 has a correlation coefficient of 0.88 with the full-length SNS-8 and a Cronbach’s alpha of 0.78.^20-22 The SNS-3 is reported as the mean on a scale from 1 to 6, with higher scores reflecting higher numeracy.

Subjective health literacy was assessed by using the 3-item Brief Health Literacy Screen (BHLS).²³ Scores range from 3 to 15, with higher scores reflecting higher literacy. Objective health literacy was assessed with the short form of the Test of Functional Health Literacy in Adults (sTOFHLA).^24,25 Scores may be categorized as inadequate (0-16), marginal (17-22), or adequate (23-36).

We assessed cognition by using the 10-item Short Portable Mental Status Questionnaire (SPMSQ).²⁶ The SPMSQ, which describes a person’s capacity for memory, structured thought, and orientation, has been validated and has demonstrated good reliability and validity.²⁷ Scores of 0 were considered to reflect intact cognition, and scores of 1 or more were considered to reflect any cognitive impairment, a scoring approach employed by other authors.²⁸ We used this approach, rather than the traditional scoring system developed by Pfeiffer et al.²⁶ (1975), because it would be the most sensitive to detect any cognitive impairment in the VICS cohort, which excluded those with severe cognition impairment, dementia, and delirium.

Covariates

During the hospitalization, participants completed an in-person interviewer-administered baseline assessment composed of demographic information, including age, self-reported race (white and nonwhite), educational attainment, home status (married, not married and living with someone, not married and living alone), and household income.

Clinical and diagnostic characteristics abstracted from the medical record included a medical history of HF, HF subtype (classified by left ventricular ejection fraction [LVEF]), coronary artery disease, chronic obstructive pulmonary disease (COPD), diabetes mellitus (DM), and comorbidity burden as summarized by the van Walraven-Elixhauser score.^29,30 Depressive symptoms were assessed during the 2 weeks prior to the hospitalization by using the first 8 items of the Patient Health Questionnaire.³¹ Scores ranged from 0 to 24, with higher scores reflecting more severe depressive symptoms. Laboratory values included estimated glomerular filtration rate (eGFR), hemoglobin (g/dl), sodium (mg/L), and brain natriuretic peptide (BNP) (pg/ml) from the last laboratory draw before discharge. Smoking status was also assessed (current and former/nonsmokers).

Hospitalization characteristics included length of stay in days, number of prior admissions in the last year, and transfer to the intensive care unit during the index admission.

Statistical Analysis

Descriptive statistics were used to summarize patient characteristics. The Kruskal-Wallis test and the Pearson χ² test were used to determine the association between patient characteristics and levels of numeracy, literacy, and cognition separately. The unadjusted relationship between patient characteristics and 30-day readmission was assessed by using Wilcoxon rank sums tests for continuous variables and Pearson χ² tests for categorical variables. In addition, a correlation matrix was performed to assess the correlations between numeracy, health literacy, and cognition (supplementary Figure 1).

To examine the association between numeracy, health literacy, and cognition and 30-day readmissions, a series of multivariable Poisson (log-linear) regression models were fit.³² Like other studies, numeracy, health literacy, and cognition were examined as categorical and continuous measures in models.³³ Each model was modified with a sandwich estimator for robust standard errors. Log-linear models were chosen over logistic regression models for ease of interpretation because (exponentiated) parameters correspond to risk ratios (RRs) as opposed to odds ratios. Furthermore, the fitting challenges associated with log-linear models when predicted probabilities are near 0 or 1 were not present in these analyses. Redundancy analyses were conducted to ensure that independent variables were not highly correlated with a linear combination of the other independent variables. To avoid case-wise deletion of records with missing covariates, we employed multiple imputation with 10 imputation samples by using predictive mean matching.^34,35 All analyses were conducted in R version 3.1.2 (The R Foundation, Vienna, Austria).³⁶

Overall, 883 patients were included in this analysis (supplementary Figure 2). Of the 883 participants, 46% were female and 76% were white (Table 1). Their median age was 60 years (interdecile range [IDR] 39-78) and the median educational attainment was 13.5 years (IDR 11-18).

This is the first study to examine the effect of numeracy alongside literacy and cognition on 30-day readmission risk among patients hospitalized with ADHF. Overall, we found that 33.9% of participants had inadequate numeracy skills, and 24.6% had inadequate or marginal health literacy. In unadjusted and adjusted models, numeracy was not associated with 30-day readmission. Although (objective) low health literacy was associated with 30-day readmission in unadjusted models, it was not in adjusted models. Additionally, though 53% of participants had any cognitive impairment, readmission did not differ significantly by this factor. Taken together, these findings suggest that other factors may be greater determinants of 30-day readmissions among patients hospitalized for ADHF.

Only 1 other study has examined the effect of numeracy on readmission risk among patients hospitalized for HF. In this multicenter prospective study, McNaughton et al.³⁷ found low numeracy to be associated with higher odds of recidivism to the emergency department (ED) or hospital within 30 days. Our findings may differ from theirs for a few reasons. First, their study had a significantly higher percentage of individuals with low numeracy (55%) compared with ours (33.9%). This may be because they did not exclude individuals with severe cognitive impairment, and their patient population was of lower socioeconomic status (SES) than ours. Low SES is associated with higher 30-day readmissions among HF patients^1,10 throughout the literature, and low numeracy is associated with low SES in other diseases.^13,38,39 Finally, they studied recidivism, which was defined as any unplanned return to the ED or hospital within 30 days of the index ED visit for acute HF. We only focused on 30-day readmissions, which also may explain why our results differed.

We found that health literacy was not associated with 30-day readmissions, which is consistent with the literature. Although an association between health literacy and mortality exists among adults with HF, several studies have not found an association between health literacy and 30- and 90-day readmission among adults hospitalized for HF.^8,9,40 Although we found an association between objective health literacy and 30-day readmission in unadjusted analyses, we did not find one in the multivariable model. This, along with our numeracy finding, suggests that numeracy and literacy may not be driving the 30-day readmission risk among patients hospitalized with ADHF.

We examined cognition alongside numeracy and literacy because it is a prevalent condition among HF patients and because it is associated with adverse outcomes among patients with HF, including readmission.^41,42 Studies have shown that HF preferentially affects certain cognitive domains,⁴³ some of which are vital to HF self-care activities. We found that 53% of patients had any cognitive impairment, which is consistent with the literature of adults hospitalized for ADHF.^44,45 Cognitive impairment was not, however, associated with 30-day readmissions. There may be a couple reasons for this. First, we measured cognitive impairment with the SPMSQ, which, although widely used and well-validated, does not assess executive function, the domain most commonly affected in HF patients with cognitive impairment.⁴⁶ Second, patients with severe cognitive impairment and those with delirium were excluded from this study, which may have limited our ability to detect differences in readmission by this factor.

As in prior studies, we found that a history of DM and more hospitalizations in the prior year were independently associated with 30-day readmissions in fully adjusted models. Like other studies, in adjusted models, we found that LVEF and a history of HF were not independently associated with 30-day readmission.^47-49 This, however, is not surprising because recent studies have shown that, although HF patients are at risk for multiple hospitalizations, early readmission after a hospitalization for ADHF specifically is often because of reasons unrelated to HF or a non-cardiovascular cause in general.^50,51

Although a negative study, several important themes emerged. First, while we were able to assess numeracy, health literacy, and cognition, none of these measures were HF-specific. It is possible that we did not see an effect on readmission because our instruments failed to assess domains specific to HF, such as monitoring weight changes, following a low-salt diet, and interpreting blood pressure. Currently, however, no HF-specific objective numeracy measure exists. With respect to health literacy, only 1 HF-specific measure exists,⁵² although it was only recently developed and validated. Second, while numeracy may not be a driving influence of all-cause 30-day readmissions, it may be associated with other health behaviors and quality metrics that we did not examine here, such as self-care, medication adherence, and HF-specific readmissions. Third, it is likely that the progression of HF itself, as well as the clinical management of patients following discharge, contribute significantly to 30-day readmissions. Increased attention to predischarge processes for HF patients occurred at VUMC during the study period; close follow-up and evidence-directed therapies may have mitigated some of the expected associations. Finally, we were not able to assess numeracy of participants’ primary caregivers who may help patients at home, especially postdischarge. Though a number of studies have examined the role of family caregivers in the management of HF,^53,54 none have examined numeracy levels of caregivers in the context of HF, and this may be worth doing in future studies.

Overall, our study has several strengths. The size of the cohort is large and there were high response rates during the follow-up period. Unlike other HF readmission studies, VICS accounts for readmissions to outside hospitals. Approximately 35% of all hospitalizations in VICS are to outside facilities. Thus, the ascertainment of readmissions to hospitals other than Vanderbilt is more comprehensive than if readmissions to VUMC were only considered. We were able to include a number of clinical comorbidities, laboratory and diagnostic tests from the index admission, and hospitalization characteristics in our analyses. Finally, we performed additional analyses to investigate the correlation between numeracy, literacy, and cognition; ultimately, we found that the majority of these correlations were weak, which supports our ability to study them simultaneously among VICS participants.

Nonetheless, we note some limitations. Although we captured readmissions to outside hospitals, the study took place at a single referral center in Tennessee. Though patients were diverse in age and comorbidities, they were mostly white and of higher SES. Finally, we used home status as a proxy for social support, which may underestimate the support that home care workers provide.

In conclusion, in this prospective longitudinal study of adults hospitalized with ADHF, inadequate numeracy was present in more than a third of patients, and low health literacy was present in roughly a quarter of patients. Neither numeracy nor health literacy, however, were associated with 30-day readmissions in adjusted analyses. Any cognitive impairment, although present in roughly one-half of patients, was not associated with 30-day readmission either. Our findings suggest that other influences may play a more dominant role in determining 30-day readmission rates in patients hospitalized for ADHF than inadequate numeracy, low health literacy, or cognitive impairment as assessed here.

Acknowledgments

This research was supported by the National Heart, Lung, and Blood Institute (R01 HL109388) and in part by the National Center for Advancing Translational Sciences (UL1 TR000445-06). The content is solely the responsibility of the authors and does not necessarily represent official views of the National Institutes of Health. The authors’ funding sources did not participate in the planning, collection, analysis, or interpretation of data or in the decision to submit for publication. Dr. Sterling is supported by T32HS000066 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. Dr. Mixon has a VA Health Services Research and Development Service Career Development Award at the Tennessee Valley Healthcare System, Department of Veterans Affairs (CDA 12-168). This material was presented at the Society of General Internal Medicine Annual Meeting on April 20, 2017, in Washington, DC.

Disclosure

Dr. Kripalani reports personal fees from Verustat, personal fees from SAI Interactive, and equity from Bioscape Digital, all outside of the submitted work. Dr. Rothman and Dr. Wallston report personal fees from EdLogics outside of the submitted work. All of the other authors have nothing to disclose