Variations in the Hospital Standardized Mortality Ratios in Korea

Objectives:

The hospital standardized mortality ratio (HSMR) has been widely used because it allows for robust risk adjustment using administrative data and is important for improving the quality of patient care.

Methods:

All inpatients discharged from hospitals with more than 700 beds (66 hospitals) in 2008 were eligible for inclusion. Using the claims data, 29 most responsible diagnosis (MRDx), accounting for 80% of all inpatient deaths among these hospitals, were identified, and inpatients with those MRDx were selected. The final study population included 703 571 inpatients including 27 718 (3.9% of all inpatients) in-hospital deaths. Using logistic regression, risk-adjusted models for predicting in-hospital mortality were created for each MRDx. The HSMR of individual hospitals was calculated for each MRDx using the model coefficients. The models included age, gender, income level, urgency of admission, diagnosis codes, disease-specific risk factors, and comorbidities. The Elixhauser comorbidity index was used to adjust for comorbidities.

Results:

For 26 out of 29 MRDx, the c-statistics of these mortality prediction models were higher than 0.8 indicating excellent discriminative power. The HSMR greatly varied across hospitals and disease groups. The academic status of the hospital was the only factor significantly associated with the HSMR.

Conclusions:

We found a large variation in HSMR among hospitals; therefore, efforts to reduce these variations including continuous monitoring and regular disclosure of the HSMR are required.     

Quantitative Prediction of Individual Psychopathology in Trauma Survivors Using Resting-State fMRI

Neuroimaging techniques hold the promise that they may one day aid the clinical assessment of individual psychiatric patients. However, the vast majority of studies published so far have been based on average differences between groups. This study employed a multivariate approach to examine the potential of resting-state functional magnetic resonance imaging (MRI) data for making accurate predictions about psychopathology in survivors of the 2008 Sichuan earthquake at an individual level. Resting-state functional MRI data was acquired for 121 survivors of the 2008 Sichuan earthquake each of whom was assessed for symptoms of post-traumatic stress disorder (PTSD) using the 17-item PTSD Checklist (PCL). Using a multivariate analytical method known as relevance vector regression (RVR), we examined the relationship between resting-state functional MRI data and symptom scores. We found that the use of RVR allowed quantitative prediction of clinical scores with statistically significant accuracy (correlation=0.32, P=0.006; mean squared error=176.88, P=0.001). Accurate prediction was based on functional activation in a number of prefrontal, parietal, and occipital regions. This is the first evidence that neuroimaging techniques may inform the clinical assessment of trauma-exposed individuals by providing an accurate and objective quantitative estimation of psychopathology. Furthermore, the significant contribution of parietal and occipital regions to such estimation challenges the traditional view of PTSD as a disorder specific to the fronto-limbic network.     

Experimental Analysis of the Enzymatic Degradation of Polycaprolactone: Microcrystalline Cellulose Composites and Numerical Method for the Prediction of the Degraded Geometry

The degradation rate of polycaprolactone (PCL) is a key issue when using this material in Tissue Engineering or eco-friendly packaging sectors. Although different PCL-based composite materials have been suggested in the literature and extensively tested in terms of processability by material extrusion additive manufacturing, little attention has been paid to the influence of the fillers on the mechanical properties of the material during degradation. This work analyses the possibility of tuning the degradation rate of PCL-based filaments by the introduction of microcrystalline cellulose into the polymer matrix. The enzymatic degradation of the composite and pure PCL materials were compared in terms of mass loss, mechanical properties, morphology and infrared spectra. The results showed an increased degradation rate of the composite material due to the presence of the filler (enhanced interaction with the enzymes). Additionally, a new numerical method for the prediction of the degraded geometry was developed. The method, based on the Monte Carlo Method in an iterative process, adjusts the degradation probability according to the exposure of each discretized element to the degradation media. This probability is also amplified depending on the corresponding experimental mass loss, thus allowing a good fit to the experimental data in relatively few iterations.     

Important Tools for Use by Pediatric Endocrinologists in the Assessment of Short Stature

Assessment and management of children with growth failure has improved greatly over recent years. However, there remains a strong potential for further improvements by using novel digital techniques. A panel of experts discussed developments in digitalization of a number of important tools used by pediatric endocrinologists at the third 360° European Meeting on Growth and Endocrine Disorders, funded by Merck KGaA, Germany, and this review is based on those discussions. It was reported that electronic monitoring and new algorithms have been devised that are providing more sensitive referral for short stature. In addition, computer programs have improved ways in which diagnoses are coded for use by various groups including healthcare providers and government health systems. Innovative cranial imaging techniques have been devised that are considered safer than using gadolinium contrast agents and are also more sensitive and accurate. Deep-learning neural networks are changing the way that bone age and bone health are assessed, which are more objective than standard methodologies. Models for prediction of growth response to growth hormone (GH) treatment are being improved by applying novel artificial intelligence methods that can identify non-linear and linear factors that relate to response, providing more accurate predictions. Determination and interpretation of insulin-like growth factor-1 (IGF-1) levels are becoming more standardized and consistent, for evaluation across different patient groups, and computer-learning models indicate that baseline IGF-1 standard deviation score is among the most important indicators of GH therapy response. While physicians involved in child growth and treatment of disorders resulting in growth failure need to be aware of, and keep abreast of, these latest developments, treatment decisions and management should continue to be based on clinical decisions. New digital technologies and advancements in the field should be aimed at improving clinical decisions, making greater standardization of assessment and facilitating patient-centered approaches.     

Mortality and Long-Term Outcome of Neonates with Congenital Heart Disease and Acute Perinatal Stroke: A Population-Based Case-Control Study

Objective: Neonates with congenital heart disease (CHD) and perinatal stroke have high mortality and survivors are at risk for poor long-term neurodevelopmental outcome. The aim of this study was to assess the risk factors and outcome of neonates with both CHD and MRI-confirmed perinatal stroke (Study Group) and compare those to the risk factors and outcome of infants matched for CHD without stroke (Control-1) and of infants matched for MRI-confirmed stroke without CHD (Control-2). Methods: We conducted a population-based case-control study enrolling 28 term neonates with CHD and MRI-confirmed acute perinatal stroke born between 2007–2017 in the Central-Hungarian Region. Each of the control groups included 56 infants. The Bayley Scales of Infant Development-II, the Brunet-Lézine test and the Binet Intelligence scales-V were used for neurodevelopmental follow-up at a median age of 61 months. Results: Mortality was highest in the Study Group (25% compared to 5% and 2%, respectively, p = 0.001). Adverse neurodevelopmental outcome was prevalent in the Study (53%) and Control-2 Groups (52%, p = 0.03). Significantly different parameters among the three groups included Apgar scores, mode of delivery, gestational age at birth, cardiac interventions and twin pregnancy. In a multivariable regression analysis adjusted for clinically relevant parameters, patients in the Study Group had significantly higher odds for mortality compared to patients in the Control-1 Group (OR: 6.5 95% CI: 1.1–39.4). Conclusions: Neonates with perinatal stroke and CHD are at a higher risk for dying compared to neonates with CHD without stroke. In addition, the stroke-associated direct insult to the brain likely plays an important role in the development of neurodevelopmental morbidity in these patients.