Development of a predictive model of hospitalization in primary care patients with heart failure

Reduced heart failure-related healthcare costs with Furoscix versus in-hospital intravenous diuresis in heart failure patients: the FREEDOM-HF study

Aim: Compare heart failure (HF) costs of Furoscix use at home compared with inpatient intravenous (IV) diuresis. Patients & methods: Prospective, case control study of chronic HF patients presenting to emergency department (ED) with worsening congestion discharged to receive Furoscix 80 mg/10 ml 5-h subcutaneous infusion for ≤7 days. 30-day HF-related costs in Furoscix group derived from commercial claims database compared with matched historical patients hospitalized for <72 h. Results: Of 24 Furoscix patients, 1 (4.2%) was hospitalized in 30-day period. 66 control patients identified and were well-matched for age, sex, ejection fraction (EF), renal function and other comorbidities. Furoscix patients had reduced mean per patient HF-related healthcare cost of $16,995 (p < 0.001). Conclusion: Furoscix use was associated with significant reductions in 30-day HF-related healthcare costs versus matched hospitalized controls.

Man vs. Machine: Comparing Physician vs. Electronic Health Record-Based Model Predictions for 30-Day Hospital Readmissions

BACKGROUND

Electronic health record (EHR)-based readmission risk prediction models can be automated in real-time but have modest discrimination and may be missing important readmission risk factors. Clinician predictions of readmissions may incorporate information unavailable in the EHR, but the comparative usefulness is unknown. We sought to compare clinicians versus a validated EHR-based prediction model in predicting 30-day hospital readmissions.

METHODS

We conducted a prospective survey of internal medicine clinicians in an urban safety-net hospital. Clinicians prospectively predicted patients' 30-day readmission risk on 5-point Likert scales, subsequently dichotomized into low- vs. high-risk. We compared human with machine predictions using discrimination, net reclassification, and diagnostic test characteristics. Observed readmissions were ascertained from a regional hospitalization database. We also developed and assessed a "human-plus-machine" logistic regression model incorporating both human and machine predictions.

RESULTS

We included 1183 hospitalizations from 106 clinicians, with a readmission rate of 20.8%. Both clinicians and the EHR model had similar discrimination (C-statistic 0.66 vs. 0.66, p = 0.91). Clinicians had higher specificity (79.0% vs. 48.9%, p < 0.001) but lower sensitivity (43.9 vs. 75.2%, p < 0.001) than EHR model predictions. Compared with machine, human was better at reclassifying non-readmissions (non-event NRI + 30.1%) but worse at reclassifying readmissions (event NRI - 31.3%). A human-plus-machine approach best optimized discrimination (C-statistic 0.70, 95% CI 0.67-0.74), sensitivity (65.5%), and specificity (66.7%).

CONCLUSION

Clinicians had similar discrimination but higher specificity and lower sensitivity than EHR model predictions. Human-plus-machine was better than either alone. Readmission risk prediction strategies should incorporate clinician assessments to optimize the accuracy of readmission predictions.

FEV1 Predicts Cardiac Status and Outcome in Chronic Heart Failure

BACKGROUND

COPD is an established predictor of clinical outcome in patients with chronic heart failure (HF). However, little evidence is available about the predictive value of FEV₁ in chronic HF.

RESEARCH QUESTION

Is pulmonary function related to the progression of chronic HF?

STUDY DESIGN AND METHODS

The MyoVasc study (ClinicalTrials.gov Identifier: NCT04064450) is a prospective cohort study of HF. Information on pulmonary and cardiac functional and structural status was obtained by body plethysmography and echocardiography. The primary study end point was worsening of HF.

RESULTS

Overall 2,998 participants (age range, 35-84 years) with available FEV₁ data were eligible for analysis. Linear multivariate regression analysis revealed an independent relationship of FEV₁ (per -1 SD) with deteriorated systolic and diastolic left ventricle (LV) function as well as LV hypertrophy under adjustment of age, sex, height, cardiovascular risk factors (CVRFs), and clinical profile (LV ejection fraction: β-estimate, -1.63% [95% CI, -2.00% to -1.26%]; E/E' ratio: β-estimate, 0.82 [95% CI, 0.64-0.99]; and LV mass/height^2.7: β-estimate, 1.58 [95% CI, 1.07-2.10]; P < .001 for all). During a median time to follow-up of 2.6 years (interquartile range, 1.1-4.1 years), worsening of HF occurred in 235 individuals. In Cox regression model adjusted for age, sex, height, CVRF, and clinical profile, pulmonary function (FEV₁ per -1 SD) was an independent predictor of worsening of HF (hazard ratio [HR], 1.44 [95% CI, 1.27-1.63]; P < .001). Additional adjustment for obstructive airway pattern and C-reactive protein mitigated, but did not substantially alter, the results underlining the robustness of the observed effect (HR_FEV1, 1.39 [95% CI, 1.20-1.61]; P < .001). The predictive value of FEV₁ was consistent across subgroups, including individuals without obstruction (HR, 1.55 [95% CI, 1.34-1.77]; P < .001) and nonsmokers (HR, 1.72 [95% CI, 1.39-1.96]; P < .001).

INTERPRETATION

FEV₁ represents a strong candidate to improve future risk stratification and prevention strategies in individuals with chronic, stable HF.

TRIAL REGISTRY

ClinicalTrials.gov; No.: NCT04064450; URL: www.clinicaltrials.gov.

Identifying patients who access musculoskeletal physical therapy: a retrospective cohort analysis

BACKGROUND

Musculoskeletal conditions are common and cause high levels of disability and costs. Physical therapy is recommended for many musculoskeletal conditions. Past research suggests that referral rates appear to have increased over time, but the rate of accessing a physical therapist appears unchanged.

OBJECTIVE

Our retrospective cohort study describes the rate of physical therapy use after referral for a variety of musculoskeletal diagnoses while comparing users and non-users of physical therapy services after referral.

METHODS

The study sample included patients in the University of Utah Health system who received care from a medical provider for a musculoskeletal condition. We included a comprehensive set of variables available in the electronic data warehouse possibly associated with attending physical therapy. Our primary analysis compared differences in patient factors between physical therapy users and non-users using Poisson regression.

RESULTS

15 877 (16%) patients had a referral to physical therapy, and 3812 (24%) of these patients accessed physical therapy after referral. Most of the factors included in the model were associated with physical therapy use except for sex and number of comorbidities. The receiver operating characteristic curve was 0.63 suggesting poor predictability of the model but it is likely related to the heterogeneity of the sample.

CONCLUSIONS

We found that obesity, ethnicity, public insurance and urgent care referrals were associated with poor adherence to physical therapy referral. However, the limited predictive power of our model suggests a need for a deeper examination into factors that influence patients access to a physical therapist.