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PURPOSE: The complexity of cancer care requires planning and analysis to achieve the highest level of quality. We aim to measure the quality of care provided to patients with non-small cell lung cancer (NSCLC) using the data contained in the hospital's information systems, in order to establish a system of continuous quality improvement. METHODS/PATIENTS: Retrospective observational cohort study conducted in a university hospital in Spain, consecutively including all patients with NSCLC treated between 2016 and 2020. A total of 34 quality indicators were selected based on a literature review and clinical practice guideline recommendations, covering care processes, timeliness, and outcomes. Applying data science methods, an analysis algorithm, based on clinical guideline recommendations, was set up to integrate activity and administrative data extracted from the Electronic Patient Record along with clinical data from a lung cancer registry. RESULTS: Through data generated in routine practice, it has been feasible to reconstruct the therapeutic trajectory and automatically calculate quality indicators using an algorithm based on clinical practice guidelines. Process indicators revealed high adherence to guideline recommendations, and outcome indicators showed favorable survival rates compared to previous data. CONCLUSIONS: Our study proposes a methodology to take advantage of the data contained in hospital information sources, allowing feedback and repeated measurement over time, developing a tool to understand quality metrics in accordance with evidence-based recommendations, ultimately seeking a system of continuous improvement of the quality of health care.
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INTRODUCTION AND OBJECTIVES: Sarcopenia is one of the most common complications of cirrhosis, associated with an increased risk of morbidity and mortality. It is therefore necessary to perform a proper nutritional evaluation in these patients. Although CT scans are the gold standard for diagnosing sarcopenia, they are not widely used in clinical practice. There is thus a need to find indirect methods for identifying sarcopenia in patients with cirrhosis. MATERIAL AND METHODS: This is a cross-sectional study consecutively including all cirrhotic outpatients who underwent CT scans. RESULTS: A total of 174 patients met all the inclusion criteria and none of exclusion criteria. Fifty-five patients (31.6%) showed sarcopenia on CT scans. Multivariate analysis revealed that the factors that were independently associated with the presence of sarcopenia on CT scans were: male sex (OR 11.27, 95% CI 3.53-35.95; p<0.001), lower body mass index (BMI) (OR 1.22, 95% CI 1.11-1.34; p<0.001) and lower phase angle by bioelectrical impedance analysis (OR 2.83, 95% CI 1.74-4.6; p<0.001). With the variables identified from the multivariate study we developed a nomogram that allows ruling out the presence of sarcopenia. Our model rules out sarcopenia with an area under the receiver operating characteristic curve value of 0.8. The cutoff point of the probability to rule out sarcopenia was 0.6 (sensitivity 85%, specificity 73%, Youden index 0.58, PPV 82.5% and NPV 91.3%). CONCLUSION: Since CT scans involve exposure to radiation and their availability is limited, we propose using this nomogram as an indirect method to rule out sarcopenia in cirrhotic patients.