Symptom Domain Groups of the Patient-Reported Outcomes Measurement Information System Tools Independently Predict Hospitalizations and Re-hospitalizations in Cirrhosis

Background Patient-Reported Outcomes Measurement Information System (PROMIS) tools can identify health-related quality of life (HRQOL) domains that could differentially affect disease progression. Cirrhotics are highly prone to hospitalizations and re-hospitalizations, but the current clinical prognostic models may be insufficient, and thus studying the contribution of individual HRQOL domains could improve prognostication. Aim Analyze the impact of individual HRQOL PROMIS domains in predicting time to all non-elective hospitalizations and re-hospitalizations in cirrhosis. Methods Outpatient cirrhotics were administered PROMIS computerized tools. The first non-elective hospitalization and subsequent re-hospitalizations after enrollment were recorded. Individual PROMIS domains significantly contributing toward these outcomes were generated using principal component analysis. Factor analysis revealed three major PROMIS domain groups: daily function (fatigue, physical function, social roles/activities and sleep issues), mood (anxiety, anger, and depression), and pain (pain behavior/impact) accounted for 77% of the variability. Cox proportional hazards regression modeling was used for these groups to evaluate time to first hospitalization and re-hospitalization. Results A total of 286 patients [57 years, MELD 13, 67% men, 40% hepatic encephalopathy (HE)] were enrolled. Patients were followed at 6-month (mth) intervals for a median of 38 mths (IQR 22–47), during which 31% were hospitalized [median IQR mths 12.5 (3–27)] and 12% were re-hospitalized [10.5 mths (3–28)]. Time to first hospitalization was predicted by HE, HR 1.5 (CI 1.01–2.5, p = 0.04) and daily function PROMIS group HR 1.4 (CI 1.1–1.8, p = 0.01), independently. In contrast, the pain PROMIS group were predictive of the time to re-hospitalization HR 1.6 (CI 1.1–2.3, p = 0.03) as was HE, HR 2.1 (CI 1.1–4.3, p = 0.03). Conclusions Daily function and pain HRQOL domain groups using PROMIS tools independently predict hospitalizations and re-hospitalizations in cirrhotic patients

Interleukin-1 blockade in recently decompensated systolic heart failure: study design of the recently decompensated heart failure anakinra response trial (RED-HART)

Heart Failure (HF) is a clinical syndrome characterized by dyspnea, fatigue, and poor exercise capacity due to impaired cardiac function. The incidence of HF is increasing and represents the leading cause of hospitalization in the United States among patients > 65 years of age. Neurohormonal blockade has proven to reduce morbidity and mortality; however the persistent toll of HF demonstrates the urgent need to continue to develop novel drugs that target other pathophysiological paradigms. The presence of inflammation in cardiovascular disease has been well-established and interleukin-1 (IL-1), the prototypical proinflammatory agent, has been shown in preclinical animal models to induce cardiac dysfunction. The current study will investigate the role of IL-1 as an inflammatory mediator of HF progression and investigate whether IL-1 blockade with anakinra, recombinant human IL-1 receptor antagonist, improves aerobic exercise performance in patients with recently decompensated systolic HF. This study will be composed of 3 treatment arms (20 patients each): 1) anakinra 100mg daily for 12 weeks; 2) anakinra 100mg daily for 2 weeks followed by placebo for 10 weeks; or 3) placebo for 12 weeks. All patients will be followed for at least 24 weeks. The co-primary endpoints will be placebo-corrected interval changes in peak oxygen consumption (VO2) and ventilatory efficiency (VE/VCO2 slope) measured by Cardiopulmonary Exercise Testing (CPX) after 2 weeks of anakinra treatment. Secondary endpoints will include interval changes in 1) CPX variables at 4, 12 and 24 weeks; 2) echocardiographic measures of cardiac dimension/function; 3) quality of life assessments; 4) inflammatory biomarkers; and 5) clinical outcome including days alive outside of the hospital and survival free of re-hospitalization for HF. The RED-HART study will be the first study to address the potential benefits of IL-1 blockade on aerobic exercise performance in patients with recently decompensated HF

Excisional treatment in women with cervical adenocarcinoma in situ (AIS): a prospective randomised controlled noninferiority trial to compare AIS persistence/recurrence after loop electrosurgical excision procedure with cold knife cone biopsy: protocol for a pilot study

Introduction: Adenocarcinoma in situ (AIS) of the uterine cervix is the precursor to invasive endocervical adenocarcinoma. An excisional biopsy such as a cold knife cone biopsy (CKC) should be performed to exclude invasive adenocarcinoma. Loop electrosurgical excision procedure (LEEP) is an alternative modality to CKC but is controversial in AIS. There is a perception that there is a greater likelihood of incomplete excision of AIS with LEEP because the depth of excised tissue tends to be smaller and the tissue margins may show thermal artefact which can interfere with pathology assessment. In the USA, guidelines recommend that any treatment modality can be used to excise AIS, provided that the specimen remains intact with interpretable margins. However, there are no high-quality studies comparing LEEP with CKC and well-designed prospective studies are needed. If such a study were to show that LEEP was non-inferior to CKC for the outcomes of post-treatment persistence, recurrence and adenocarcinoma, LEEP could be recommended as an appropriate treatment option for AIS in selected patients. This would benefit women because, unlike CKC, LEEP does not require general anaesthesia and may be associated with reduced morbidity. Methods and analysis: The proposed exploratory study is a parallel group trial with an allocation ratio of 2:1 in favour of the intervention (LEEP: CKC). Participants are women aged ≥18 to ≤45 years diagnosed with AIS on cervical screening and/or colposcopically directed biopsy in Australia and New Zealand, who are to receive excisional treatment in a tertiary level centre. Ethics and dissemination: Ethical approval for the study has been granted by the St John of God Healthcare Human Research Ethics Committee (reference number #1137)

Ketorolac Use and Incidence of Postoperative Bleeding in an ERAS Colorectal Surgical Population: A Quality Analysis of Practice

Background Ketorolac is an effective analgesic adjunct and is currently used in Enhanced Recovery After Surgery (ERAS) protocols. However, investigation into its safety profile is warranted in specific surgical populations. This Quality Improvement (QI) study sought to examine the association of ketorolac to increased postoperative bleeding risk, increased postoperative renal impairment, and 30-day readmission within an ERAS protocol for colorectal surgery. Methods A retrospective review was conducted of 158 patients enrolled in an existing ERAS protocol for colorectal surgery with at least one dose of ketorolac administered in the perioperative period. Outcomes of postoperative bleeding, 30-day readmission, and preoperative/postoperative serum creatinine levels were assessed. Results There was no statistically significant difference in the incidence of postoperative bleeding compared to a known population. There was a significant association of 30-day readmissions with documented evidence of bleeding (P = 0.037). There was no significant change in the preoperative and postoperative serum creatinine. Multivariate logistic regression analysis found no association of postoperative bleeding with pre-existing chronic non-steroidal anti-inflammatory drug (NSAID) use or preoperative serum creatinine. Conclusions Ketorolac is not associated with an increased risk of postoperative bleeding in colorectal ERAS surgical patients. However, postoperative bleeding does predict the likelihood for 30-day readmissions

Short-term safety outcomes of mastectomy and immediate pre-pectoral implant-based breast reconstruction:Pre-BRA prospective multicentre cohort study

Background: Prepectoral breast reconstruction (PPBR) has recently been introduced to reduce postoperative pain and improve cosmetic outcomes in women having implant-based procedures. High-quality evidence to support the practice of PPBR, however, is lacking. Pre-BRA is an IDEAL stage 2a/2b study that aimed to establish the safety, effectiveness, and stability of PPBR before definitive evaluation in an RCT. The short-Term safety endpoints at 3 months after surgery are reported here. Methods: Consecutive patients electing to undergo immediate PPBR at participating UK centres between July 2019 and December 2020 were invited to participate. Demographic, operative, oncology, and complication data were collected. The primary outcome was implant loss at 3 months. Other outcomes of interest included readmission, reoperation, and infection. Results: Some 347 women underwent 424 immediate implant-based reconstructions at 40 centres. Most were single-stage direct-To-implant (357, 84.2 per cent) biological mesh-Assisted (341, 80.4 per cent) procedures. Conversion to subpectoral reconstruction was necessary in four patients (0.9 per cent) owing to poor skin-flap quality. Of the 343 women who underwent PPBR, 144 (42.0 per cent) experienced at least one postoperative complication. Implant loss occurred in 28 women (8.2 per cent), 67 (19.5 per cent) experienced an infection, 60 (17.5 per cent) were readmitted for a complication, and 55 (16.0 per cent) required reoperation within 3 months of reconstruction. Conclusion: Complication rates following PPBR are high and implant loss is comparable to that associated with subpectoral mesh-Assisted implant-based techniques. These findings support the need for a well-designed RCT comparing prepectoral and subpectoral reconstruction to establish best practice for implant-based breast reconstruction

Protocol for a mixed-methods exploratory investigation of care following intensive care discharge: the REFLECT study

© Author(s) 2019. Re-use permitted under CC BY. Published by BMJ.INTRODUCTION: A substantial number of patients discharged from intensive care units (ICUs) subsequently die without leaving hospital. It is unclear how many of these deaths are preventable. Ward-based management following discharge from ICU is an area that patients and healthcare staff are concerned about. The primary aim of REFLECT (Recovery Following Intensive Care Treatment) is to develop an intervention plan to reduce in-hospital mortality rates in patients who have been discharged from ICU. METHODS AND ANALYSIS: REFLECT is a multicentre mixed-methods exploratory study examining ward care delivery to adult patients discharged from ICU. The study will be made up of four substudies. Medical notes of patients who were discharged from ICU and subsequently died will be examined using a retrospective case records review (RCRR) technique. Patients and their relatives will be interviewed about their post-ICU care, including relatives of patients who died in hospital following ICU discharge. Staff involved in the care of patients post-ICU discharge will be interviewed about the care of this patient group. The medical records of patients who survived their post-ICU stay will also be reviewed using the RCRR technique. The analyses of the substudies will be both descriptive and use a modified grounded theory approach to identify emerging themes. The evidence generated in these four substudies will form the basis of the intervention development, which will take place through stakeholder and clinical expert meetings. ETHICS AND DISSEMINATION: Ethical approval has been obtained through the Wales Research and Ethics Committee 4 (17/WA/0107). We aim to disseminate the findings through international conferences, international peer-reviewed journals and social media. TRIAL REGISTRATION NUMBER: ISRCTN14658054.Peer reviewedFinal Published versio

Development and evaluation of ensemble-based classification models for predicting unplanned hospital readmissions after hysterectomy

Unplanned hospital readmissions are a key indicator of quality in healthcare and can lead to high unnecessary costs for the hospital due to additional required resources or reduced payments by insurers or governments. Predictive analytics can support the identification of patients at high-risk for readmission early on to enable timely interventions. In Australia, hysterectomies present the 2nd highest observed readmission rates of all surgical procedures in public hospitals. Prior research so far only focuses on developing explanatory models to identify associated risk factors for past patients. In this study, we develop and compare 24 prediction models using state-of-the-art sampling and ensemble methods to counter common problems in readmission prediction, such as imbalanced data and poor performance of individual classifiers. The application and evaluation of these models are presented, resulting in an excellent predictive power with under- and oversampling and an additional slight increase in performance when combined with ensemble methods

An explanatory machine learning framework for studying pandemics: The case of COVID-19 emergency department readmissions

ArticleInPressOne of the major challenges that confront medical experts during a pandemic is the time required to identify and validate the risk factors of the novel disease and to develop an effective treatment protocol. Traditionally, this process involves numerous clinical trials that may take up to several years, during which strict preventive measures must be in place to control the outbreak and reduce the deaths. Advanced data analytics techniques, however, can be leveraged to guide and speed up this process. In this study, we combine evolutionary search algorithms, deep learning, and advanced model interpretation methods to develop a holistic exploratory- predictive-explanatory machine learning framework that can assist clinical decision-makers in reacting to the challenges of a pandemic in a timely manner. The proposed framework is showcased in studying emergency department (ED) readmissions of COVID-19 patients using ED visits from a real-world electronic health records database. After an exploratory feature selection phase using genetic algorithm, we develop and train a deep artificial neural network to predict early (i.e., 7-day) readmissions (AUC = 0.883). Lastly, a SHAP model is formulated to estimate additive Shapley values (i.e., importance scores) of the features and to interpret the magnitude and direction of their effects. The findings are mostly in line with those reported by lengthy and expensive clinical trial studies

Jefferson Digital Commons quarterly report: July-September 2017

This quarterly report includes: Assorted articles Confronting Racism, Bias, and Social Injustice in Healthcare Lecture CREATE DAY OT Capstone presentations Grand Rounds Assorted Newsletters Nexus Maximus Posters University Wide Posters House Staff Quality Improvement and Patient Safety Posters CSHLA Scholar Day posters Student publications and presentations What People are Saying about the Jefferson Digital Common