Get Them Out! Self-Administered IV Antibiotics at Home The - - PowerPoint PPT Presentation

Get Them Out!

Self-Administered IV Antibiotics at Home

The Importance of a Propensity Score

Robert W. Haley, MD Division of Epidemiology University of Texas Southwestern Medical Center Dallas

No Disclosures

The Problem

• Every hospital has patients who require long-

term IV antimicrobial infusion.

– Staph septicemia – Bacterial endocarditis – Diabetic foot – Osteomyelitis

• After the initial workup, the patients occupy a

hospital bed only to receive the infusion.

• Insured or charity patients can be discharged

to receive infusions in an LTAC or home health nurse, but uninsured patients stay in hospital.

Hospital days

42 3

Intensity

• f services

Disadvantages of Long-term Antimicrobial Infusion in Hospital

Burden on safety-net hospitals Burden on patients

Risk of complications (e.g., infection)

Definition of OPAT

• Outpatient Parenteral Antimicrobial Therapy
• Provision of IV antibiotic therapy in at least 2

doses on different days outside the hospital.

• Goals

– Allow patients to complete treatment safely and effectively in the comfort of their home or another

• utpatient site

– Avoid the inconveniences, complications, and expense of hospitalization

Models of outpatient parenteral antimicrobial therapy (OPAT) delivery

Paladino J A , and Poretz D. CID 2010;51:S198-S208

Definition of OPAT

• Outpatient Parenteral Antimicrobial Therapy
• Provision of IV antibiotic therapy in at least 2

doses on different days outside the hospital.

• Goals

– Allow patients to complete treatment safely and effectively in the comfort of their home or another

• utpatient site

– Avoid the inconveniences, complications, and expense

• f hospitalization
• But not available to patients without funding (e.g.,

private insurance, Medicare, Medicaid or other local funding option).

Kavita Bhavan, MD

“Let unfunded patients do it themselves”

S-OPAT

Self-Administered Outpatient Parenteral Antimicrobial Therapy

S-OPAT Program

Intervention

• Developed program in 2009 as an alternative for

uninsured patients to complete long-term antibiotic therapy at home comparable to services received in traditional funded settings.

• Patients undergo bedside teaching and competency

assessment prior to discharge from hospital.

• Transitioned from the hospital into a dedicated post-

discharge OPAT clinic, and followed weekly by nurses for PICC line care and at fixed intervals by physicians to assess clinical response to therapy.

Best Practice Methods

• Dedicated multidisciplinary OPAT team: Physician,

Pharm D, Care Management, RN

• Effective multilingual patient education material at

the appropriate level of health literacy and employ the “teach back method” for bedside teaching

• Standardized core competency tool to test and

record patient’s ability to self-administer IV antibiotics

11

Incorporating Patient Safety into Transition of Care

Coaching patients for successful outcomes

Patient Education

Best Practice Methods

Schillinger D, Piette J, Grumbach K, Wang F, Wilson C, Daher C, Leong-Grotz K, Castro C, Bindman A. Closing the Loop Physician Communication With Diabetic Patients Who Have Low Health Literacy. Arch Intern Med/Vol 163, Jan 13, 2003

Teach-Back: Closing the Loop

Testing for Competency

Teaching Tools

Specific Instructions

Preparing Antibiotics

Wire coat hanger

Infusion by Gravity

Weekly Followup in S-OPAT Clinic

Study to Evaluate S-OPAT Objective

Determine whether indigent, often poorly educated and mostly non-English-speaking patients can self-administer long-term IV antibiotics at home (S-OPAT) as safely and effectively as traditionally accepted models of

• utpatient care by a healthcare practitioner

available to patients with funding (H-OPAT)

Outcomes

• Compared patients treated in S-OPAT with those

treated in H-OPAT on 2 outcomes

– 30-day readmission rate – 1-year mortality rate

• Calculated total number of hospital bed days

avoided, as reflected by number of days a patient self-administered parenteral antibiotic therapy as an outpatient under the S-OPAT program

Controlling for Selection Bias

• An observational study (non-randomized)
• Patients in the S-OPAT and H-OPAT groups differed
• n several important measures.

– Healthcare funding (insurance, Medicare, Medicaid) – Language – Nationality and US citizenship – Educational level – Age

• These differences created a strong potential

for selection bias in the outcome.

• Must control for this in the analysis.

Two Approaches in a Multivariable Logistic Regression Analysis

• Enter Covariates into the multivariable analysis
• Enter a Propensity Score into the multivariable

analysis

Two Approaches in a Multivariable Logistic Regression Analysis

• Enter Covariates into the multivariable analysis

– Age – Sex – Race – Country of origin – Source of payment – Education level – Income

• Enter a Propensity Score into the multivariable

analysis

Two Approaches in a Multivariable Logistic Regression Analysis

• Enter Covariates into the multivariable analysis

– Age – Sex – Race – Country of origin – Source of payment – Education level – Income

• Enter a Propensity Score into the multivariable

analysis

Covariates control confounding, but not necessarily selection bias.

Two Approaches in a Multivariable Logistic Regression Analysis

• Enter Covariates into the multivariable analysis

– Age – Sex – Race – Country of origin – Source of payment – Education level – Income

• Enter a Propensity Score into the multivariable

analysis

Covariates control confounding, but not necessarily selection bias. A propensity score measures each patient’s propensity (probability) of receiving the treatment (S-OPAT) based on their characteristics.

• R. Haley, Epi for Clin Investigator

Propensity Score

• Definition: An individual patient’s probability
• f receiving the treatment conditional on

measured covariates.

– “How do you develop a propensity score?”

• R. Haley, Epi for Clin Investigator

Development of Propensity Score

– Instead of putting covariates into the main multivariable logistic regression model:

Outcome = Treatment Covar-1 Covar-2 . . . Covar-I (output is the odds ratio)

– Develop a logistic regression model of the treatment:

Treatment = Determinant-1 Determinant-2 . . . Determinant-I / pred=PS And output the probability of treatment conditional on the determinant

• variables. This is the PS (a continuous variable = probability of

treatment)

– Within the strata of the PS, the probability of getting the treatment is the same.

Development of Propensity Score

– Instead of putting covariates into the main multivariable logistic regression model:

Outcome = Treatment Covar-1 Covar-2 . . . Covar-I PS (output is the odds ratio)

– Develop a logistic regression model of the treatment:

Treatment = Determinant-1 Determinant-2 . . . Determinant-I / pred=PS And output the probability of treatment conditional on the determinant

• variables. This is the PS (a continuous variable = probability of

treatment)

– Within the strata of the PS, the probability of getting the treatment is the same.

• R. Haley, Epi for Clin Investigator

4 Ways to Use the Propensity Score

• PS Covariate Adjustment (most used):

– Introduce the PS into the multivariable logistic model of outcome as a new covariate.

• PS Stratification:

– Stratify the outcome analysis on the PS.

• PS Matching:

– Match

• PS Weighting:

– Weight the multivariable logistic model of

• utcome with the inverse PS.

Summary of Patient Selection

a Patients who were homeless, had a history of IV drug abuse, or were medically unstable b The eligibility criteria are given in the appendix of the paper.

a b

Logistic Regression Model of S-OPAT vs H-OPAT to Develop the Propensity Score

Outcome variable is S-OPAT vs H-OPAT

Multivariable Proportional Hazards Regression

Model of 30-day Readmission

Model 1 controls for confounding by entering covariates into the model.

Multivariable Proportional Hazards Regression

Model of 30-day Readmission

Model 1 controls for confounding by entering covariates into the model. The OR of 0.59 indicates that S-OPAT had a 41% lower 30- day readmission rate than H-OPAT. % reduction = 1 - OR

1 – 0.59 = 41% reduction

Multivariable Proportional Hazards Regression

Model of 30-day Readmission

Model 1 controls for confounding by entering covariates into the model. Model 2 also controls for selection bias by entering the propensity score into the model.

1 – 0.59 = 41% reduction

Multivariable Proportional Hazards Regression

Model of 30-day Readmission

Model 1 controls for confounding by entering covariates into the model. Model 2 also controls for selection bias by entering the propensity score into the model.

1 – 0.59 = 41% reduction 1 – 0.53 = 47% reduction

Multivariable Proportional Hazards

Regression Model of 1-Year Mortality

Model 1 controls for confounding by entering covariates into the model. Model 2 also controls for selection bias by entering the propensity score into the model.

1 – 0.94 = 6% reduction 1 – 0.86 = 14% reduction

Multivariable Proportional Hazards

Regression Model of 1-Year Mortality

Model 1 controls for confounding by entering covariates into the model. Model 2 also controls for selection bias by entering the propensity score into the model.

Readmissions During Infusion Period

Readmissions for reason: S-OPAT H-OPAT Related to IV infusion 21/944 ( 2.2%) 4/244 ( 1.8%) NS Unrelated 131/944 (13.9%) 49/244 (21.9%) NS

Reasons related to IV infusion 13 PICC line dysfunction 10 Underlying infection not improving 6 Renal or hepatic toxicity from antibiotic 4 Catheter-related bloodstream infection 1 Deep vein thrombosis

Impact of the S-OPAT Program on the Hospital’s Inpatient Bed Utilization

*Before the S-OPAT clinic was started, all of these days would have been spent in the hospital just to receive antimicrobial infusions.

*

30 day readmission 47% lower for “self-care” (S-OPAT) population vs pts receiving standard of care (H-OPAT)

27,666 inpatient days

avoided over 4 years

~ $40,000,000 saved

for hospital Improved resource utilization by freeing beds for acutely ill

What do Patients Value ?

• Ability to return to work earlier
• Ability to care for dependents at

home

• Avoid high cost of hospitalization
• Ability to complete therapy safely in

the comfort of home environment with minimal interruption of daily life

Patient-Centered Outcomes Survey

Lessons Learned

• S-OPAT model delivers safe and effective

care outside of the hospital setting.

• A multi-disciplinary approach is critical to

the successful implementation of this transition of care model.

• Systematic “teach-back” training and weekly

clinic followup important and cost-effective.

• Prior Perception vs Patients’ Ability to self

administer IV therapy through program of patient engagement and empowerment

Questions?