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In the validation cohort, the RF model (AUROC 0.890) and GLM model (AUROC 0.864) achieved good to excellent prediction of LOS, but only marginally better than system averages in practice.<br /><br /> Regional sharing of patient data allowed for effective prediction of LOS across systems; however, this only provided marginal improvement over hospital averages at the aggregate level. A federated approach of sharing aggregated system capacity and average LOS will likely allow for effective capacity management at the regional level.<br /><br />Regional sharing of patient data allowed for effective prediction of LOS across systems; however, this only provided marginal improvement over hospital averages at the aggregate level. A federated approach of sharing aggregated system capacity and average LOS will likely allow for effective capacity management at the regional level.<br /><br /> Wearable fitness devices are increasingly being used by the general population, with many new applications being proposed for healthy adults as well as for adults with chronic diseases. Fewer, if any, studies of these devices have been conducted in healthy adolescents and teenagers, especially over a long period of time. The goal of this work was to document the successes and challenges involved in 5 years of a wearable fitness device use in a pediatric case study.<br /><br /> Comparison of 5 years of step counts and minutes asleep from a teenaged girl and her father.<br /><br /> At 60 months, this may be the longest reported pediatric study involving a wearable fitness device, and the first simultaneously involving a parent and a child. We find step counts to be significantly higher for both the adult and teen on school/work days, along with less sleep. The teen walked significantly less towards the end of the 5-year study. Surprisingly, many of the adult's and teen's sleeping and step counts were correlated, possibly due to coordinated behaviors.<br /><br /> We end with several recommendations for pediatricians and device manufacturers, including the need for constant adjustments of stride length and calorie counts as teens are growing.<br /><br /> With periodic adjustments for growth, this pilot study shows these devices can be used for more accurate and consistent measurements in adolescents and teenagers over longer periods of time, to potentially promote healthy behaviors.<br /><br />With periodic adjustments for growth, this pilot study shows these devices can be used for more accurate and consistent measurements in adolescents and teenagers over longer periods of time, to potentially promote healthy behaviors.<br /><br /> Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We aimed to conduct a systematic literature review (SLR) of studies that applied machine learning (ML) methods to clinical data derived from electronic health records in order to model risk for progression of AD dementia.<br /><br /> We searched for articles published between January 1, 2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital Library, Association for Computing Machinery Digital Library, and arXiv. We used predefined criteria to select relevant articles and summarized them according to key components of ML analysis such as data characteristics, computational algorithms, and research focus.<br /><br /> There has been a considerable rise over the past 5 years in the number of research papers using ML-based analysis for AD dementia modeling. We reviewed 64 relevant articles in our SLR. The results suggest that majority of existesults can enhance the impact, adaptation, and generalizability of this research.<br /><br /> The development of clinical predictive models hinges upon the availability of comprehensive clinical data. Tapping into such resources requires considerable effort from clinicians, data scientists, and engineers. Specifically, these efforts are focused on data extraction and preprocessing steps required prior to modeling, including complex database queries. A handful of software libraries exist that can reduce this complexity by building upon data standards. However, a gap remains concerning electronic health records (EHRs) stored in star schema clinical data warehouses, an approach often adopted in practice. In this article, we introduce the FlexIBle EHR Retrieval (FIBER) tool a Python library built on top of a star schema (i2b2) clinical data warehouse that enables flexible generation of modeling-ready cohorts as data frames.<br /><br /> FIBER was developed on top of a large-scale star schema EHR database which contains data from 8 million patients and over 120 million encounters. To illustrate FIBER's capabilities, we present its application by building a heart surgery patient cohort with subsequent prediction of acute kidney injury (AKI) with various machine learning models.<br /><br /> Using FIBER, we were able to build the heart surgery cohort (<br /><br /> = 12 061), identify the patients that developed AKI (<br /><br /> = 1005), and automatically extract relevant features (<br /><br /> = 774). Finally, we trained machine learning models that achieved area under the curve values of up to 0.77 for this exemplary use case.<br /><br /> FIBER is an open-source Python library developed for extracting information from star schema clinical data warehouses and reduces time-to-modeling, helping to streamline the clinical modeling process.<br /><br />FIBER is an open-source Python library developed for extracting information from star schema clinical data warehouses and reduces time-to-modeling, helping to streamline the clinical modeling process.[This corrects the article DOI 10.1093/jamiaopen/ooab008.].In this phase 3 trial, older patients with acute myeloid leukemia ineligible for intensive chemotherapy were randomized 21 to receive the polo-like kinase inhibitor, volasertib (V; 350 mg intravenous on days 1 and 15 in 4-wk cycles), combined with low-dose cytarabine (LDAC; 20 mg subcutaneous, twice daily, days 1-10; n = 444), or LDAC plus placebo (P; n = 222). [https://www.selleckchem.com/products/680c91.html Metabolism inhibitor] Primary endpoint was objective response rate (ORR); key secondary endpoint was overall survival (OS). Primary ORR analysis at recruitment completion included patients randomized ≥5 months beforehand; ORR was 25.2% for V+LDAC and 16.8% for P+LDAC (n = 371; odds ratio 1.66 [95% confidence interval (CI), 0.95-2.89]; P = 0.071). At final analysis (≥574 OS events), median OS was 5.6 months for V+LDAC and 6.5 months for P+LDAC (n = 666; hazard ratio 0.97 [95% CI, 0.8-1.2]; P = 0.757). The most common adverse events (AEs) were infections/infestations (grouped term; V+LDAC, 81.3%; P+LDAC, 63.5%) and febrile neutropenia (V+LDAC, 60.4%; P+LDAC, 29.3%). Fatal AEs occurred in 31.2% with V+LDAC versus 18.0% with P+LDAC, most commonly infections/infestations (V+LDAC, 17.1%; P+LDAC, 6.3%). Lack of OS benefit with V+LDAC versus P+LDAC may reflect increased early mortality with V+LDAC from myelosuppression and infections.Glaucoma is a serious complication after corneal transplantation and itself a common cause for graft failure and leading cause of vision loss post-keratoplasty due to corneal endothelial decompensation. Endothelial keratoplasty procedures like Descemet stripping automated endothelial keratoplasty (DSAEK) and Descemet membrane endothelial keratoplasty (DMEK) may be superior to penetrating keratoplasty (PK) regarding the incidence of elevated intraocular pressure (IOP) and development of glaucoma. There are indications that regardless of the method of keratoplasty, some corneal diseases like pseudophakic bullous keratopathy, corneal perforation, and graft rejection have a higher risk for developing post-keratoplasty glaucoma than keratoconus and corneal dystrophies and likewise respond less to IOP lowering therapy. In this review, the pathophysiology of post-keratoplasty glaucoma, the diagnostic tools with focus on different devices, and their limitations with regard to measuring IOP and the treatment modalities are presented.<br /><br /> Diphoterine<br /><br /> is an amphoteric irrigating solution armed with rapid pH-neutralising action. It serves as an effective first-aid treatment for managing chemical burns, including chemical eye injury (CEI). However, its use is not widely adopted in current clinical practice, primarily attributed to limited clinical evidence. This study aims to highlight the experience in using Diphoterine for managing CEI in a UK tertiary referral centre.<br /><br /> This retrospective case series included all patients who presented with CEI and treated with Diphoterine at the James Cook University Hospital, UK, between April 2018 and February 2020.<br /><br /> Seven patients (10 eyes) were included; the mean age was 28.2 ± 17.0 years (ranged, 3-70 years) and 85.7% were male. All patients presented with an alkaline injury with a mean presenting pH of 8.7 ± 0.7 and a median (±interquartile range [IQR]) corrected-distance visual acuity (CDVA) of 0.10 ± 0.28 logMAR. Based on Roper-Hall classification, 90% and 10% of the eyes were of grade-I and -IV rst case series in the United Kingdom, reporting the use of Diphoterine in managing CEI. The rapid pH-neutralising action of Diphoterine, with less volume required, makes it an ideal initial treatment for efficiently managing adult and paediatric patients with CEI in clinics.Higher rates of cancer treatment toxicity and uniquely poor outcomes following a cancer diagnosis have been reported for persons living with HIV (PLWH). This highlights the importance of active HIV status ascertainment in the oncology setting. Self-disclosure of HIV via electronic questionnaire at patient intake is a low-cost option that has not been thoroughly evaluated. We examined 10 years (2009-2019) of patient intake questionnaire data at Moffitt Cancer Center. Self-disclosure of an HIV diagnosis was not uniform, with 36.1% (n = 299, 95% confidence interval [CI] = 32.8% to 39.4%) of 828 patients disclosing. Identification of HIV through this method was highest for anal cancer patients (66.7%, 95% CI = 57.8% to 74.7%). Self-disclosure among patients with hematopoietic malignancies, the most common diagnosis among PLWH at our institution, was lower (19.4%, 95% CI = 14.6% to 25.0%). Patient characteristics associated with HIV self-disclosure included cancer site, natal gender, and race and ethnicity. Findings highlight gaps to motivate future efforts to increase HIV ascertainment prior to initiating cancer care.Cancer treatment-related cardiotoxicity (ie, heart failure, coronary artery disease, vascular diseases, arrhythmia) is a growing cancer survivorship concern within oncology practice; heart disease is the leading cause of noncancer death in cancer survivors and surpasses cancer as the leading cause of death for some cancers with higher survival rates. The issue of cardiotoxicity introduces a critical tradeoff that must be acknowledged and reconciled in clinical oncology practice treating cancer aggressively and effectively in the present vs preventing future cardiotoxicity. Although many cancers must be treated as aggressively as possible, for others, multiple treatment options are available. Yet even when effective and less cardiotoxic treatments are available, they are not always chosen. Wariness to choose equally effective but less cardiotoxic treatment options may result in part from providers' and patients' reliance on "cognitive heuristics," or mental shortcuts that people (including, research shows, medical professionals) use to simplify complex judgments.