Early Post-Hospitalization Hemoglobin Recovery and Clinical Outcomes in Survivors of Critical Illness: A Population-Based Cohort Study

Anemia is common during critical illness, is associated with adverse clinical outcomes, and often persists after hospitalization. The goal of this investigation is to assess the relationships between post-hospitalization hemoglobin recovery and clinical outcomes after survival of critical illness. This is a population-based observational study of adults (≥18 years) surviving hospitalization for critical illness between January 1, 2010 and December 31, 2016 in Olmsted County, Minnesota, United States with hemoglobin concentrations and clinical outcomes assessed through one-year post-hospitalization. Multi-state proportional hazards models were utilized to assess the relationships between 1-month post-hospitalization hemoglobin recovery and hospital readmission or death through one-year after discharge. Among 6460 patients that survived hospitalization for critical illness during the study period, 2736 (42%) were alive, not hospitalized, and had available hemoglobin concentrations assessed at 1-month post-index hospitalization. Median (interquartile range) age was 69 (56, 80) years with 54% of male gender. Overall, 86% of patients had anemia at the time of hospital discharge, with median discharge hemoglobin concentrations of 10.2 (9.1, 11.6) g/dL. In adjusted analyses, each 1 g/dL increase in 1-month hemoglobin recovery was associated with decreased instantaneous hazard for hospital readmission (HR 0.87 [95% CI 0.84-0.90]; p < 0.001) and lower mortality (HR 0.82 [95% CI 0.75-0.89]; p < 0.001) through one-year post-hospitalization. The results were consistent in multiple pre-defined sensitivity analyses. Impaired early post-hospitalization hemoglobin recovery is associated with inferior clinical outcomes in the first year of survival after critical illness. Additional investigations are warranted to evaluate these relationships.

Assessment of Data Quality Variability across Two EHR Systems through a Case Study of Post-Surgical Complications

Translation of predictive modeling algorithms into routine clinical care workflows faces challenges in the form of varying data quality-related issues caused by the heterogeneity of electronic health record (EHR) systems. To better understand these issues, we retrospectively assessed and compared the variability of data produced from two different EHR systems. We considered three dimensions of data quality in the context of EHR-based predictive modeling for three distinct translational stages: model development (data completeness), model deployment (data variability), and model implementation (data timeliness). The case study was conducted based on predicting post-surgical complications using both structured and unstructured data. Our study discovered a consistent level of data completeness, a high syntactic, and moderate-high semantic variability across two EHR systems, for which the quality of data is context-specific and closely related to the documentation workflow and the functionality of individual EHR systems.

Improving the delivery of palliative care through predictive modeling and healthcare informatics

OBJECTIVE

Access to palliative care (PC) is important for many patients with uncontrolled symptom burden from serious or complex illness. However, many patients who could benefit from PC do not receive it early enough or at all. We sought to address this problem by building a predictive model into a comprehensive clinical framework with the aims to (i) identify in-hospital patients likely to benefit from a PC consult, and (ii) intervene on such patients by contacting their care team.

MATERIALS AND METHODS

Electronic health record data for 68 349 inpatient encounters in 2017 at a large hospital were used to train a model to predict the need for PC consult. This model was published as a web service, connected to institutional data pipelines, and consumed by a downstream display application monitored by the PC team. For those patients that the PC team deems appropriate, a team member then contacts the patient's corresponding care team.

RESULTS

Training performance AUC based on a 20% holdout validation set was 0.90. The most influential variables were previous palliative care, hospital unit, Albumin, Troponin, and metastatic cancer. The model has been successfully integrated into the clinical workflow making real-time predictions on hundreds of patients per day. The model had an "in-production" AUC of 0.91. A clinical trial is currently underway to assess the effect on clinical outcomes.

CONCLUSIONS

A machine learning model can effectively predict the need for an inpatient PC consult and has been successfully integrated into practice to refer new patients to PC.

Early Detection of Post-Surgical Complications using Time-series Electronic Health Records

Models predicting health complications are increasingly attempting to reflect the temporally changing nature of patient status. However, both the practice of medicine and electronic health records (EHR) have yet to provide a true longitudinal representation of a patient's medical history as relevant data is often asynchronous and highly missing. To match the stringent requirements of many static time models, time-series data has to be truncated, and missing values in samples have to be filled heuristically. However, these data preprocessing procedures may unconsciously misinterpret real-world data, and eventually lead into failure in practice. In this work, we proposed an augmented gated recurrent unit (GRU), which formulate both missingness and timeline signals into GRU cells. Real patient data of post-operative bleeding (POB) after Colon and Rectal Surgery (CRS) was collected from Mayo Clinic EHR system to evaluate the effectiveness of proposed model. Conventional models were also trained with imputed dataset, in which event missingness or asynchronicity were approximated. The performance of proposed model surpassed current state-of-the-art methods in this POB detection task, indicating our model could be more eligible to handle EHR datasets.

Eating disturbance and sexual trauma in childhood and adulthood

OBJECTIVE

This study examined the relationship between sexual trauma and eating disorder behavior, taking into account the effects of developmental stage of the victim and the effects of multiple sexual assaults.

METHOD

Four groups of adult women took part in this study. Subjects were either victims of childhood sexual abuse, victims of rape in adulthood, victims of both childhood sexual abuse and rape, and controls who were not traumatized sexually. All subjects were assessed with semistructured interviews and self-report inventories that assessed eating disorder behavior, general psychopathology, and impulsivity.

RESULTS

Victims of childhood sexual abuse differed from controls on measures of eating disorder behavior and individuals who had experienced both childhood sexual abuse and rape in adulthood were most likely to display eating disorder-related psychopathology. Victims of childhood sexual abuse also distinguished themselves with high levels of eating disorder behavior plus multiple forms of impulsive self-destructive behavior.

DISCUSSION

This study provides additional support for the association between childhood sexual abuse and eating disorder behavior. Childhood sexual abuse may be particularly linked to the presence of binge eating behavior and several other forms of impulsive self-destructive behavior.

Pathways mediating sexual abuse and eating disturbance in children

OBJECTIVE

To examine the relationship between childhood maltreatment and eating disorders in a sample of children.

METHOD

Twenty 10-15-year-old female children who were receiving treatment following reported childhood sexual abuse and 20 age-matched controls were compared on a series of measures assessing eating disorder behaviors, body image concerns, substance use, mood, impulsive behavior, and self-concept.

RESULTS

Sexually abused children reported higher levels of eating disorder behaviors, impulsive behaviors, and drug abuse than controls. Furthermore, behavioral impulsivity provided the strongest mediational effect between a history of childhood sexual abuse and purging and restrictive dieting behavior. Drug use proved to be a significant secondary mediator of the childhood sexual abuse eating disorder behavior association.

DISCUSSION

These data support the hypothesis that childhood sexual abuse is related to disordered eating in children, and extend similar findings that have been previously reported with adults. Behavioral impulsivity and drug use appear to be significant mechanisms that influence eating disorder behavior following childhood sexual abuse.