Preoperative Prediction of Non-Home Discharge: A Strategy to Reduce Resource Use after Cardiac Surgery

Pattakos, Gregory; Johnston, Douglas R.; Houghtaling, Penny L.; Nowicki, Edward R.; Blackstone, Eugene H.

doi:10.1016/j.jamcollsurg.2011.11.003

Cited by 45 publications

(28 citation statements)

References 15 publications

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“…Among patients undergoing joint arthroplasty and liver transplantation, predicting postoperative care through the use of a predictive perioperative tool appears to increase the rate of home discharge and patient satisfaction, and decrease the length of hospital stay (37, 38). As these tools have been newly established in their respective surgical domains, their impact on improving health-related costs and quality of life is yet to be determined (26, 37, 38). …”

Section: Discussionmentioning

confidence: 99%

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Risk-Scoring Model for Prediction of Non-Home Discharge in Epithelial Ovarian Cancer Patients

AlHilli

Tran

Langstraat

et al. 2013

Journal of the American College of Surgeons

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Section: Discussionmentioning

confidence: 99%

“…Efforts have been made in orthopedic surgery, cardiac surgery, and urology to develop predictive models for non-home discharge which can be used as part of routine preoperative evaluation (25, 26). However, no data on preoperative predictors of discharge destination in EOC exist.…”

Section: Introductionmentioning

confidence: 99%

Risk-Scoring Model for Prediction of Non-Home Discharge in Epithelial Ovarian Cancer Patients

AlHilli

Tran

Langstraat

et al. 2013

Journal of the American College of Surgeons

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“…This finding may have important implications for health care and Medicaid costs―an estimated 44% of adults age ≥85 are discharged to an intermediate‐ or long‐term care facility after hospitalization . It has been suggested that the ability to predict a patient's discharge disposition can allow for improved discharge planning and reduction of hospital costs and resource use . Even so, it is important to keep in mind that walk time was not related to major outcome or to any complications by any means of analysis (univariate or multivariate, continuous or dichotomous) in our study.…”

Section: Discussionmentioning

confidence: 82%

The Effects of Frailty in Patients Undergoing Elective Cardiac Surgery

Holmes

Halpin

et al. 2016

J Card Surg

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Background: The Society of Thoracic Surgeons (STS) recommends using gait speed as a marker of frailty to identify cardiac surgery patients at risk for adverse outcomes. However, a single marker of frailty may not provide consistently reliable risk information. We evaluated the impact of frailty and gait speed on patient outcomes after elective cardiac surgery. Methods: This was a prospective study of 167 older (!65 years) coronary artery bypass grafting (CABG) and/or valve surgery patients. Patients were assessed using Cardiovascular Health Study (CHS) Frailty Index criteria: weight loss, exhaustion, physical activity, gait speed, and grip strength. Results: Frailty was identified in 39 patients (23%) using CHS criteria. Frail patients had longer median intensive care unit stays (54 vs. 28 h, p = 0.003), longer median length of stay (8 vs. 5 days, p < 0.001), and greater likelihood of STS-defined complications (54% vs. 32%, p = 0.011) and discharge to an intermediate-care facility (45% vs. 12%, p < 0.001) but were not different from nonfrail patients on major outcome, operative mortality, or readmissions. After multivariate adjustment, frail and nonfrail patients were similar on perioperative outcomes. Absolute gait speed and slow gait speed using a cutoff were not related to incidence of STS-defined complications or major outcome in multivariate analyses. However, higher body mass index was correlated with slower gait speed (r s = 0.30, p < 0.001). Conclusions: The CHS index did not identify ''frail'' patients at increased risk for adverse outcomes. No relationship was found between gait speed and outcome. There is a need for alternative multidimensional measures to assess frailty in cardiac surgical patients.

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“…The unexpected nature of EGS limits opportunities for preoperative optimization, leading to high rates of non‐home discharge, months away from home even after discharge, and high rates of hospital encounters, particularly in patients with frailty. Although preoperative prediction models have been proposed to identify patients at high risk for non‐home discharge after surgery, these models do not include frailty assessments and were largely developed for patients undergoing elective surgery . Including frailty screens during the EGS hospitalization can have two benefits.…”

Section: Discussionmentioning

confidence: 91%

The Impact of Frailty on Long‐Term Patient‐Oriented Outcomes after Emergency General Surgery: A Retrospective Cohort Study

Lee

Streid

Sturgeon

et al. 2020

J American Geriatrics Society

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OBJECTIVES: Few studies examine the impact of frailty on long-term patient-oriented outcomes after emergency general surgery (EGS). We measured the prevalence of frailty among older EGS patients and examined the impact of frailty on 1-year outcomes. DESIGN: Retrospective cohort study using 2008 to 2014 Medicare claims. SETTING: Acute care hospitals. PARTICIPANTS: Patients 65 years or older who received one of the five EGS procedures with the highest mortality burden (partial colectomy, small bowel resection, peptic ulcer disease repair, adhesiolysis, or laparotomy). MEASUREMENTS: A validated claims-based frailty index (CFI) identified patients who were not frail (CFI < .15), prefrail (.15 ≤ CFI < .25), mildly frail (.25 ≤ CFI < .35), and moderately to severely frail (CFI ≥ .35). Multivariable Cox regression compared 1-year mortality. Multivariable Poisson regression compared rates of post-discharge hospital encounters (hospitalizations, intensive care unit stay, emergency department visit) and home time over 1 year after discharge. All regression models adjusted for age, sex, race, admission from facility, procedure, sepsis, inpatient palliative care delivery, trauma center designation, hospital bed size, and teaching status, and they were clustered by patient and hospital referral region. RESULTS: Among 468 459 older EGS adults, 37.4% were pre-frail, 12.4% were mildly frail, and 3.6% were moderately to severely frail. Patients with mild frailty experienced a higher risk of 1-year mortality compared with non-frail patients (hazard ratio = 1.97; confidence interval [CI] = 1.94-2.01). In the year after discharge, patients with mild and moderate to severe frailty had more hospital encounters compared with non-frail patients (7.8 and 11.5 vs 2.0 per person-year; incidence rate ratio [IRR] = 4.01; CI = 3.93-4.08 vs IRR = 5.89; CI = 5.70-6.09, respectively). Patients with mild and moderate to severe frailty also had fewer days at home in the year after discharge compared with non-frail patients (256 and 203 vs 302 mean days; IRR = .97; CI = .96-.97 vs IRR = .95; CI = .94-.95, respectively). CONCLUSION: Older EGS patients with frailty are at increased risk for poor 1-year outcomes and decreased home time. Targeted interventions for older EGS patients with frailty during the index EGS hospitalization are urgently needed to improve long-term outcomes.

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Preoperative Prediction of Non-Home Discharge: A Strategy to Reduce Resource Use after Cardiac Surgery

Cited by 45 publications

References 15 publications

Risk-Scoring Model for Prediction of Non-Home Discharge in Epithelial Ovarian Cancer Patients

Risk-Scoring Model for Prediction of Non-Home Discharge in Epithelial Ovarian Cancer Patients

The Effects of Frailty in Patients Undergoing Elective Cardiac Surgery

The Impact of Frailty on Long‐Term Patient‐Oriented Outcomes after Emergency General Surgery: A Retrospective Cohort Study

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