Low back pain as the presenting sign in a patient with primary extradural melanoma of the thoracic spine - A metastatic disease 17 Years after complete surgical resection

Primary spinal melanomas are extremely rare lesions. In 1906, Hirschberg reported the first primary spinal melanoma, and since then only 40 new cases have been reported. A 47-year-old man was admitted suffering from low back pain, fatigue and loss of body weight persisting for three months. He had a 17-year-old history of an operated primary spinal melanoma from T7-T9, which had remained stable for these 17 years. Routine laboratory findings and clinical symptoms aroused suspicion of a metastatic disease. Multislice computed tomography and magnetic resonance imaging revealed stage-IV melanoma with thoracic, abdominal and skeletal metastases without the recurrence of the primary process. Transiliac crest core bone biopsy confirmed the diagnosis of metastatic melanoma. It is important to know that in all cases of back ore skeletal pain and unexplained weight loss, malignancy must always be considered in the differential diagnosis, especially in the subjects with a positive medical history. Patients who have back, skeletal, or joint pain that is unresponsive to a few weeks of conservative treatment or have known risk factors with or without serious etiology, are candidates for imaging studies. The present case demonstrates that complete surgical resection alone may result in a favourable outcome, but regular medical follow-up for an extended period, with the purpose of an early detection of a metastatic disease, is highly recommended

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask

Coincident with the tsunami of COVID-19-related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field

International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach

Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort study

Background: While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking. Methods: A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1–365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021. Findings: Advanced age (HR 2.77, 95%CI 2.53–3.04, p < 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03–4.17, p < 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55–5.00, p < 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14–1.39, p < 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37–0.46, p < 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17–1.54, p < 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20–1.58, p < 0.0001), male sex (HR 1.67, 95%CI 1.45–1.93, p < 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80–13.91, p < 0.0001), and hypertension (HR 1.22, 95%CI 1.10–1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32–1.67) and 365 days (RR 1.54, 95%CI 1.21–1.96) compared to COVID-19 patients with no AKI. Interpretation: COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery. Funding: Authors are supported by various funders, with full details stated in the acknowledgement section

Whatever happens to trauma patients who leave against medical advice?

Although trauma patients are frequently discharged against medical advice (AMA), the fate of these patients remains mostly unknown.Patients with traumatic injuries were identified in the California State Inpatient Database, 2007 to 2011. Readmission characteristics of patients discharged AMA were compared with patients discharged home.There were 203,756 (75.65%) patients discharged home and 4,480 (1.66%) discharged AMA. Compared with those discharged home, patients discharged AMA had significantly higher 30-day readmission rates (17.12% vs 6.75%), rates of multiple readmissions (3.83% vs 1.12%), and likelihood of being readmitted at different hospitals (44.83% vs 33.82%) (all P &lt; .001). The commonest reasons for readmission in patients discharged AMA were psychiatric conditions [adjusted odds ratio: 1.67 (1.21 to 2.27)].Discharge AMA is associated with multiple readmissions and higher rates of readmissions at different hospitals. Early identification of vulnerable patients and improved modalities to prevent discharge AMA among these patients may reduce the negative outcomes associated with discharge AMA among trauma patients

The truth about trauma readmissions

There is a paucity of data on the causes and associated patient factors for unplanned readmissions among trauma patients.We examined patients admitted for traumatic injuries between 2007 and 2011 in the California State Inpatient Database. Using chi-square tests and multivariate logistic regression models, we determined rates, reasons, locations, and patient factors associated with 30-day readmissions.Among 252,752 trauma discharges, the overall readmission rate was 7.56%, with 36% of readmissions occurring at a hospital different from the hospital of initial admission. Predictors of readmissions included being discharged against medical advice (odds ratio [OR]: 2.56 [2.35 to 2.76]); Charlson scores ≥2 (OR: 2.00 [1.91 to 2.10]); and age ≥45 years (OR: 1.29 [1.25 to 1.33]). Major reasons for readmissions were musculoskeletal complaints (22.29%), psychiatric conditions (9.40%), and surgical infections (6.69%).Health and social vulnerabilities influence readmission among trauma patients, with many readmitted at other hospitals. Targeted interventions among high-risk patients may reduce readmissions after traumatic injuries