ObjectiveTo systematically review the efficacy of palliative care in heart failure patients. MethodsPubMed, EMbase, CINAHL, The Cochrane Library, VIP, CNKI, CBM and WanFang Data databases were electronically searched to collect randomized controlled trials (RCTs) on the efficacy of palliative care in heart failure patients from inception to September 2021. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies; then, meta-analysis was performed using RevMan 5.3 software. ResultsA total of 11 RCTs involving 912 patients were included. The results of meta-analysis showed that palliative care could improve the quality of life of patients with heart failure (KCCQ & McGill QoL: SMD=0.85, 95%CI 0.13 to 1.58, P=0.02; MLHFQ: SMD=−1.32, 95%CI −2.10 to −0.54, P=0.000 9), reduce the level of depression (SMD=−0.58, 95%CI −0.87 to −0.28, P=0.000 1) and anxiety (SMD=−0.51, 95%CI −0.89 to −0.13, P=0.008), improve the adverse symptoms (SMD=−1.46, 95%CI −2.67 to −0.24, P=0.02), reduce the readmission rate (RR=0.64, 95%CI 0.42 to 0.98, P=0.04) and the per hospitalization time (MD=−0.94, 95%CI −1.28 to −0.60, P<0.000 01). However, it had no obvious effect on the mortality of patients (RR=1.00, 95%CI 0.63 to 1.57, P=0.99). ConclusionCurrent evidence shows that palliative care can improve the quality of life, emotional state and adverse symptoms of patients with heart failure, and reduce the length of hospital stay and readmission rate. Due to limited quality and quantity of the included studies, more high-quality studies are required to verify the above conclusion.
ObjectiveTo construct a demand model for electronic medical record (EMR) data quality in regards to the lifecycle in machine learning (ML)-based disease risk prediction, to guide the implementation of EMR data quality assessment. MethodsReferring to the lifecycle in ML-based predictive model, we explored the demand for EMR data quality. First, we summarized the key data activities involved in each task on predicting disease risk with ML through a literature review. Second, we mapped the data activities in each task to the associated requirements. Finally, we clustered those requirements into four dimensions. ResultsWe constructed a three-layer structured ring to represent the demand model for EMR data quality in ML-based disease risk prediction research. The inner layer shows the seven main tasks in ML-based predictive models: data collection, data preprocessing, feature representation, feature selection and extraction, model training, model evaluation and optimization, and model deployment. The middle layer is the key data activities in each task; and the outer layer represents four dimensions of data quality requirements: operability, completeness, accuracy, and timeliness. ConclusionThe proposed model can guide real-world EMR data governance, improve its quality management, and promote the generation of real-world evidence.