European value-based healthcare benchmarking: moving from theory to practice

BACKGROUND

Value-based healthcare (VBHC) is a conceptual framework to improve the value of healthcare by health, care-process and economic outcomes. Benchmarking should provide useful information to identify best practices and therefore a good instrument to improve quality across healthcare organizations. This paper aims to provide a proof-of-concept of the feasibility of an international VBHC benchmarking in breast cancer, with the ultimate aim of being used to share best practices with a data-driven approach among healthcare organizations from different health systems.

METHODS

In the VOICE community-a European healthcare centre cluster intending to address VBHC from theory to practice-information on patient-reported, clinical-related, care-process-related and economic-related outcomes were collected. Patient archetypes were identified using clustering techniques and an indicator set following a modified Delphi was defined. Benchmarking was performed using regression models controlling for patient archetypes and socio-demographic characteristics.

RESULTS

Six hundred and ninety patients from six healthcare centres were included. A set of 50 health, care-process and economic indicators was distilled for benchmarking. Statistically significant differences across sites have been found in most health outcomes, half of the care-process indicators, and all economic indicators, allowing for identifying the best and worst performers.

CONCLUSIONS

To the best of our knowledge, this is the first international experience providing evidence to be used with VBHC benchmarking intention. Differences in indicators across healthcare centres should be used to identify best practices and improve healthcare quality following further research. Applied methods might help to move forward with VBHC benchmarking in other medical conditions.

Health Outcomes with Curative and Palliative Therapies in Real World: Role of the Quality of Life Summary Score in Thoracic Oncology Patients

BACKGROUND

For patients receiving therapy with curative or palliative intent for a thoracic malignancy, prediction of quality of life (QOL), once therapy starts, remains challenging. The role of health assessments by the patient instead of the doctor herein remains ill-defined.

AIMS

To assess the evolution of QOL in patients with thoracic malignancies treated with curative and palliative intent, respectively. To identify factors that determine QOL one year after the start of cancer therapy. To identify factors that affect survival.

METHODS

We prospectively included consecutive patients with a thoracic malignancy who were starting anti-cancer therapy and measured QOL with QLQ-C30 before the start of therapy, and thereafter at regular intervals for up to 12 months. A multivariate regression analysis of the global health score (GHS) and QOL summary scores (QSS) one year after the start of therapy was conducted. A proportional hazards Cox regression was conducted to investigate the effects of case-mix variables on survival.

RESULTS

Of 587 new patients, 375 started different forms of therapy. Most had non-small cell lung cancer (n = 298), 35 had small cell lung cancer, and 42 had other thoracic malignancies or were diagnosed on imaging alone. There were 203 who went for a curative intent and 172 for a palliative intent strategy. The WHO score of 0-1 was more prevalent in the former group (p = 0.02), and comorbidities were equally distributed. At baseline, all QOL indices were better in the curative group (p < 0.05). The curative group was characterized by a significant worsening of GHS and QSS (p < 0.05). The palliative group was characterized by an improvement in GHS and emotional health (p < 0.05), while other dimensions of functioning remained stable. GHS at 12 months was estimated in a multivariate linear regression model (R2 = 0.23-p < 0.001) based on baseline GHS, QSS, and comorbidity burden. QSS at 12 months was estimated (R2 = 0.31-p < 0.001) by baseline QSS and therapeutic intent strategy (curative vs. palliative). The prognostic factors for overall survival were the type of therapy (curative vs. palliative intent, p < 0.001) and occurrence of early toxicity-related hospitalization (grade ≥ 3, p = 0.001).

CONCLUSION

Patients with thoracic malignancies treated with curative intent experience a worsening of their QOL in the first year, whereas those receiving palliative anti-cancer therapy do not. QOL one year after the start of therapy depends on the baseline health scores as determined by the patient, comorbidity burden, and therapeutic strategy. Survival depends on therapeutic strategy and early hospitalization due to toxicity.

NMR-Metabolomics Reveals a Metabolic Shift after Surgical Resection of Non-Small Cell Lung Cancer

Background: Lung cancer can be detected by measuring the patient’s plasma metabolomic profile using nuclear magnetic resonance (NMR) spectroscopy. This NMR-based plasma metabolomic profile is patient-specific and represents a snapshot of the patient’s metabolite concentrations. The onset of non-small cell lung cancer (NSCLC) causes a change in the metabolite profile. However, the level of metabolic changes after complete NSCLC removal is currently unknown. Patients and methods: Fasted pre- and postoperative plasma samples of 74 patients diagnosed with resectable stage I-IIIA NSCLC were analyzed using 1H-NMR spectroscopy. NMR spectra (s = 222) representing two preoperative and one postoperative plasma metabolite profile at three months after surgical resection were obtained for all patients. In total, 228 predictors, i.e., 228 variables representing plasma metabolite concentrations, were extracted from each NMR spectrum. Two types of supervised multivariate discriminant analyses were used to train classifiers presenting a strong differentiation between the pre- and postoperative plasma metabolite profiles. The validation of these trained classification models was obtained by using an independent dataset. Results: A trained multivariate discriminant classification model shows a strong differentiation between the pre- and postoperative NSCLC profiles with a specificity of 96% (95% CI [86–100]) and a sensitivity of 92% (95% CI [81–98]). Validation of this model results in an excellent predictive accuracy of 90% (95% CI [77–97]) and an AUC value of 0.97 (95% CI [0.93–1]). The validation of a second trained model using an additional preoperative control sample dataset confirms the separation of the pre- and postoperative profiles with a predictive accuracy of 93% (95% CI [82–99]) and an AUC value of 0.97 (95% CI [0.93–1]). Metabolite analysis reveals significantly increased lactate, cysteine, asparagine and decreased acetate levels in the postoperative plasma metabolite profile. Conclusions: The results of this paper demonstrate that surgical removal of NSCLC generates a detectable metabolic shift in blood plasma. The observed metabolic shift indicates that the NSCLC metabolite profile is determined by the tumor’s presence rather than donor-specific features. Furthermore, the ability to detect the metabolic difference before and after surgical tumor resection strongly supports the prospect that NMR-generated metabolite profiles via blood samples advance towards early detection of NSCLC recurrence.

Right Heart Failure in a Patient with Critical Pulmonary Stenosis, Absent Right Pulmonary Artery, and Lung Cancer

Patient: Female, 67-year-old

Final Diagnosis: Absence of right pulmonary artery • lung cancer • pulmonary stenosis

Symptoms: Abdomen distension • dyspnea • fatigue • right heart failure

Medication: —

Clinical Procedure: Percutaneous pulmonary valve implantation • radiation therapy • right heart catherization

Specialty: Cardiology • Pulmonology

Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests

The interpretation of pulmonary function tests (PFTs) to diagnose respiratory diseases is built on expert opinion that relies on the recognition of patterns and the clinical context for detection of specific diseases. In this study, we aimed to explore the accuracy and interrater variability of pulmonologists when interpreting PFTs compared with artificial intelligence (AI)-based software that was developed and validated in more than 1500 historical patient cases.120 pulmonologists from 16 European hospitals evaluated 50 cases with PFT and clinical information, resulting in 6000 independent interpretations. The AI software examined the same data. American Thoracic Society/European Respiratory Society guidelines were used as the gold standard for PFT pattern interpretation. The gold standard for diagnosis was derived from clinical history, PFT and all additional tests.The pattern recognition of PFTs by pulmonologists (senior 73%, junior 27%) matched the guidelines in 74.4±5.9% of the cases (range 56-88%). The interrater variability of κ=0.67 pointed to a common agreement. Pulmonologists made correct diagnoses in 44.6±8.7% of the cases (range 24-62%) with a large interrater variability (κ=0.35). The AI-based software perfectly matched the PFT pattern interpretations (100%) and assigned a correct diagnosis in 82% of all cases (p<0.0001 for both measures).The interpretation of PFTs by pulmonologists leads to marked variations and errors. AI-based software provides more accurate interpretations and may serve as a powerful decision support tool to improve clinical practice.

Health Outcomes for Definite Concurrent Chemoradiation in Locally Advanced Non-Small Cell Lung Cancer: A Prospective Study

BACKGROUND

In patients with locally advanced lung cancer treated with concurrent chemoradiation, outcome measurements have been mostly limited to survival.

OBJECTIVES

We aimed to measure outcomes that matter to these patients beyond survival in a general clinical practice.

METHODS

In a prospective single-centre study, consecutive patients with locally advanced non-small cell lung cancer reported their own outcomes using the EORTC Quality of Life Questionnaire Core 30 at baseline, during therapy, at therapy stop and till 1 year after therapy end every 3 months. Survival, complications, quality of death and case-mix variables were measured.

RESULTS

There were 32 consecutive patients included prospectively from June 2013 until September 2016. Median overall survival was 24.3 months (95% CI 12.7-35.9). Severe toxicity (grade III-IV) was frequent (haematologic toxicity III-IV in 59%). Patient-reported outcomes (PROs) documented the burden on global health status and on functional domains (physical, role, social, emotional and cognitive functioning). Deterioration was pronounced during and after treatment with drops over 20 up to 40% points from baseline for physical, role and social functioning. Clinically meaningful negative effects did persist up to 6 and 9 months for physical and role functioning. Fifty-six percent of the deceased patients died in hospital.

CONCLUSIONS

The assault on health-related quality of life during concurrent chemoradiation for locally advanced lung cancer is considerable. Loss of physical and role functioning persists up to 6 and 9 months after therapy end, respectively. Measuring PROs can help to identify issues for improvement of the value of care delivered.

Successful minimally invasive emergency surgery for descending necrotising mediastinitis

We present a case of descending necrotising mediastinitis in a healthy young patient, complicated by pneumonia of the right inferior lobe of the lung with parapneumonic effusions. We describe a successful outcome following adequate antibiotics, effective surgical drainage by thoracoscopy and parasternotomy, and hyperbaric oxygen therapy.