Integrated cardiothoracic surgery match: Trends among applicants compared with other surgical subspecialties.

OBJECTIVE: The objective of this study was to evaluate trends, qualifications, race/ethnicity, and gender of applicants to integrated cardiothoracic (CT I-6) residency programs and compare them with other competitive surgical subspecialties. METHODS: Data were collected from the National Residency Matching Program, Electronic Residency Application Service, and Association of American Medical Colleges for thoracic surgery, orthopedic surgery, neurological surgery, otolaryngology (ENT), plastic surgery, and vascular surgery for 2010 t0 2020. Applicant gender, race/ethnicity, Alpha Omega Alpha (AOA) membership, United States Medical Licensing Examination scores, research productivity, and graduation from a top-40 medical school were analyzed. RESULTS: From 2010 to 2020, CT I-6 experienced growth in postgraduate year 1 positions (280.0%), total applicants (62.2%), and US senior applicants (59.2%). No growth in CT I-6 positions (38) or programs (29) occurred from 2016 to 2020. CT I-6 had the lowest match rates among total applicants (31.7%) and US seniors (41.0%) in 2020. CT I-6 had fewer female applicants compared with ENT (P < .001) and plastic surgery (P < .001), but more than orthopedic surgery (P < .001). Although most CT I-6 US applicants self-identified as White (75.0%), there were more Asian applicants compared with applicants for orthopedic surgery (P < .001), ENT (P < .001), plastic surgery (P < .001), and neurological surgery (P < .01). Matched applicants averaged the highest Step 2-Clinical Knowledge scores (255.1), AOA membership (48.5%), and graduation rates from top-40 medical schools (54.5%). CONCLUSIONS: Despite tremendous growth in positions, CT I-6 has consistently been the most difficult surgical subspecialty to match. CT I-6 has recently attracted an increasingly diverse applicant pool. For the 2019 to 2020 National Residency Matching Program Match Cycle, successful applicants had the highest Step 2-Clinical Knowledge scores, AOA membership rates, and graduation rates from a top-40 medical school among all surgical subspecialties examined.

CT-guided percutaneous bilateral sacroiliac joint arthroplasty.

Pelvic fractures and sacroiliac joint (SI) diastasis are debilitating injuries which can drastically decrease an individual's functional capacity, and lead to significant morbidity and mortality. In younger, healthier populations, pelvic fractures are usually the result of sudden traumatic forces, such as a motor vehicle collision. Atraumatic pelvic fractures can also occur, and are most commonly due to age related changes and osteoporosis. Even with prompt surgical management, pelvic fractures often result in a host of challenging complications such as disruption of the SI joint with subsequent limitations in mobility and chronic pain. In this case report, we present a novel treatment of SI diastasis, secondary to corticosteroid induced osteoporosis and pelvic fractures. We performed percutaneous bilateral poly-methyl-methacrylate (PMMA) SI joint fusions under CT-guidance. Here we describe a case of bilateral SI joint CT guided percutaneous arthroplasty for osteoporotic SI joint diastasis and pelvic fractures, resulting in a rapid resolution of SI joint pain and restoration of the ability to ambulate in a previously wheelchair-bound patient. This may be of particular benefit in patients who are poor surgical candidates who experience osteoporotic fractures and would be otherwise unable to receive definitive operative management of their pathologic fractures.

Development and External Validation of a Machine Learning Tool to Rule Out COVID-19 Among Adults in the Emergency Department Using Routine Blood Tests: A Large, Multicenter, Real-World Study.

BACKGROUND: Conventional diagnosis of COVID-19 with reverse transcription polymerase chain reaction (RT-PCR) testing (hereafter, PCR) is associated with prolonged time to diagnosis and significant costs to run the test. The SARS-CoV-2 virus might lead to characteristic patterns in the results of widely available, routine blood tests that could be identified with machine learning methodologies. Machine learning modalities integrating findings from these common laboratory test results might accelerate ruling out COVID-19 in emergency department patients. OBJECTIVE: We sought to develop (ie, train and internally validate with cross-validation techniques) and externally validate a machine learning model to rule out COVID 19 using only routine blood tests among adults in emergency departments. METHODS: Using clinical data from emergency departments (EDs) from 66 US hospitals before the pandemic (before the end of December 2019) or during the pandemic (March-July 2020), we included patients aged ≥20 years in the study time frame. We excluded those with missing laboratory results. Model training used 2183 PCR-confirmed cases from 43 hospitals during the pandemic; negative controls were 10,000 prepandemic patients from the same hospitals. External validation used 23 hospitals with 1020 PCR-confirmed cases and 171,734 prepandemic negative controls. The main outcome was COVID 19 status predicted using same-day routine laboratory results. Model performance was assessed with area under the receiver operating characteristic (AUROC) curve as well as sensitivity, specificity, and negative predictive value (NPV). RESULTS: Of 192,779 patients included in the training, external validation, and sensitivity data sets (median age decile 50 [IQR 30-60] years, 40.5% male [78,249/192,779]), AUROC for training and external validation was 0.91 (95% CI 0.90-0.92). Using a risk score cutoff of 1.0 (out of 100) in the external validation data set, the model achieved sensitivity of 95.9% and specificity of 41.7%; with a cutoff of 2.0, sensitivity was 92.6% and specificity was 59.9%. At the cutoff of 2.0, the NPVs at a prevalence of 1%, 10%, and 20% were 99.9%, 98.6%, and 97%, respectively. CONCLUSIONS: A machine learning model developed with multicenter clinical data integrating commonly collected ED laboratory data demonstrated high rule-out accuracy for COVID-19 status, and might inform selective use of PCR-based testing.

Soil-transmitted helminth eggs assessment in wastewater in an urban area in India.

Water quality and sanitation are inextricably linked to prevalence and control of soil-transmitted helminth infections, a public health concern in resource-limited settings. India bears a large burden of disease associated with poor sanitation. Transformative onsite sanitation technologies are being developed that feature elimination of pathogens including helminth eggs in wastewater treatment. We are conducting third-party testing of multiple sanitation technology systems in Coimbatore (Tamil Nadu) India. To ensure stringent testing of the pathogen removal ability of sanitation technologies, the presence of helminth eggs in wastewater across the town of Coimbatore was assessed. Wastewater samples from existing test sites as well as desludging trucks servicing residential and non-residential septic tanks, were collected. The AmBic methodology (based on washing, sieving, sedimenting and floating) was used for helminth egg isolation. We tested 29 different source samples and found a 52% prevalence of potentially infective helminth eggs. Identification and enumeration of helminth species is reported against the septage source (private residential vs. shared toilet facility) and total solids content. Trichuris egg counts were higher than those of hookworm and Ascaris from desludging trucks, whereas hookworm egg counts were higher in fresh wastewater samples. Surprisingly, no correlation between soil transmitted helminth eggs and total solids was observed.

Characterization of fecal sludge as biomass feedstock for thermal treatment in the southern Indian state of Tamil Nadu.

Background: Transformative sanitation technologies aim to treat fecal sludge (FS) by thermal processes and recover resources from it. There is a paucity of data describing the relevant properties of FS as viable feedstock for thermal treatment in major geographical target areas, such as India. Methods: This study characterized FS collected from septic tanks in two cities located in the Indian southern state of Tamil Nadu. FS samples were obtained at the point of discharge from trucks in Tiruppur (n=85 samples) and Coimbatore (n=50 samples). Additionally, biosolids obtained from sewage treatment plants (STP) in the cities of Coimbatore and Madurai were characterized. Total solids (TS) were measured, and proximate and ultimate analysis were conducted according to methods used by the fuel industry. Additionally, the ash content was analyzed for heavy metal using standard methods. Results: The average higher heating value (HHV) across all FS samples in Tiruppur (13.4 MJ/kg) was significantly higher than in Coimbatore (5.4 MJ/kg), which was partially attributed to the high ash content of 69% in the latter samples.  The HHV of the biosolids samples ranged from 10 to 12.2 MJ/Kg. The average total solids (TS) content for FS was 3.3% and 2.0% for Tiruppur and Coimbatore respectively, while the median TS content for the two cities was 2.3% and 1.2%. The heavy metal content of the ash was found to be below the thresholds for land disposal. Conclusions: This is one of the first studies that has systematically characterized the calorific and mineral content of septage and biosolids in several cities in India. We expect these data to serve as input data in the design of thermal processes for fecal sludge treatment.