RESUMEN
BACKGROUND: Due to its high impact on quality of life and mental health, close monitoring and often psychotherapy is recommended for patients with a ventricular assist device (VAD). This study investigates the psychological comorbidity and the corresponding psychotherapeutic treatment situation of VAD patients. Special attention is also given to the professional perspective VAD team (assistant and senior cardiologists and specialized nurses). METHODS: We conducted a cross-sectional observational study. Data from 50 VAD patients (mean age = 53.52, standard deviation = 13.82 years, 84.0% male) and their VAD team were analyzed. The presence of a psychological disorder was evaluated by structured clinical interviews for DSM-IV (SCID-I-Interviews). Patients answered a questionnaire regarding their current psychotherapeutic treatment status and their attitude towards psychotherapy. The VAD team answered a questionnaire about the patients' needs for psychotherapy and indicated whether they addressed this topic with the patient. Data were analyzed descriptively, by analysis of variance and t-test. RESULTS: A total of 58% of VAD patients suffered from at least one significant psychological disorder, 79.3% of those were not in psychotherapy. The VAD team could not identify the patients who suffered from a psychological disorder (F = 1.90; p = 0.18). They perceived more need for psychotherapy than they addressed with their patients (T = 3.39; p < 0.001). CONCLUSIONS: While there is a high psychological morbidity among VAD patients, only few receive psychotherapy. Psychological comorbidity is not easily detected by the VAD team. Standardized psychosocial care could be implemented by regular psychological assessments and further information of patients and their VAD teams.
RESUMEN
BACKGROUND: Patients with heart failure (HF) are the most commonly readmitted group of adult patients in Germany. Most patients with HF are readmitted for noncardiovascular reasons. Understanding the relevance of HF management outside the hospital setting is critical to understanding HF and factors that lead to readmission. Application of machine learning (ML) on data from statutory health insurance (SHI) allows the evaluation of large longitudinal data sets representative of the general population to support clinical decision-making. OBJECTIVE: This study aims to evaluate the ability of ML methods to predict 1-year all-cause and HF-specific readmission after initial HF-related admission of patients with HF in outpatient SHI data and identify important predictors. METHODS: We identified individuals with HF using outpatient data from 2012 to 2018 from the AOK Baden-Württemberg SHI in Germany. We then trained and applied regression and ML algorithms to predict the first all-cause and HF-specific readmission in the year after the first admission for HF. We fitted a random forest, an elastic net, a stepwise regression, and a logistic regression to predict readmission by using diagnosis codes, drug exposures, demographics (age, sex, nationality, and type of coverage within SHI), degree of rurality for residence, and participation in disease management programs for common chronic conditions (diabetes mellitus type 1 and 2, breast cancer, chronic obstructive pulmonary disease, and coronary heart disease). We then evaluated the predictors of HF readmission according to their importance and direction to predict readmission. RESULTS: Our final data set consisted of 97,529 individuals with HF, and 78,044 (80%) were readmitted within the observation period. Of the tested modeling approaches, the random forest approach best predicted 1-year all-cause and HF-specific readmission with a C-statistic of 0.68 and 0.69, respectively. Important predictors for 1-year all-cause readmission included prescription of pantoprazole, chronic obstructive pulmonary disease, atherosclerosis, sex, rurality, and participation in disease management programs for type 2 diabetes mellitus and coronary heart disease. Relevant features for HF-specific readmission included a large number of canonical HF comorbidities. CONCLUSIONS: While many of the predictors we identified were known to be relevant comorbidities for HF, we also uncovered several novel associations. Disease management programs have widely been shown to be effective at managing chronic disease; however, our results indicate that in the short term they may be useful for targeting patients with HF with comorbidity at increased risk of readmission. Our results also show that living in a more rural location increases the risk of readmission. Overall, factors beyond comorbid disease were relevant for risk of HF readmission. This finding may impact how outpatient physicians identify and monitor patients at risk of HF readmission.