Harnessing the Electronic Health Record and Computerized Provider Order Entry Data for Resource Management During the COVID-19 Pandemic: Development of a Decision Tree.

BACKGROUND: The COVID-19 pandemic has resulted in shortages of diagnostic tests, personal protective equipment, hospital beds, and other critical resources. OBJECTIVE: We sought to improve the management of scarce resources by leveraging electronic health record (EHR) functionality, computerized provider order entry, clinical decision support (CDS), and data analytics. METHODS: Due to the complex eligibility criteria for COVID-19 tests and the EHR implementation-related challenges of ordering these tests, care providers have faced obstacles in selecting the appropriate test modality. As test choice is dependent upon specific patient criteria, we built a decision tree within the EHR to automate the test selection process by using a branching series of questions that linked clinical criteria to the appropriate SARS-CoV-2 test and triggered an EHR flag for patients who met our institutional persons under investigation criteria. RESULTS: The percentage of tests that had to be canceled and reordered due to errors in selecting the correct testing modality was 3.8% (23/608) before CDS implementation and 1% (262/26,643) after CDS implementation (P<.001). Patients for whom multiple tests were ordered during a 24-hour period accounted for 0.8% (5/608) and 0.3% (76/26,643) of pre- and post-CDS implementation orders, respectively (P=.03). Nasopharyngeal molecular assay results were positive in 3.4% (826/24,170) of patients who were classified as asymptomatic and 10.9% (1421/13,074) of symptomatic patients (P<.001). Positive tests were more frequent among asymptomatic patients with a history of exposure to COVID-19 (36/283, 12.7%) than among asymptomatic patients without such a history (790/23,887, 3.3%; P<.001). CONCLUSIONS: The leveraging of EHRs and our CDS algorithm resulted in a decreased incidence of order entry errors and the appropriate flagging of persons under investigation. These interventions optimized reagent and personal protective equipment usage. Data regarding symptoms and COVID-19 exposure status that were collected by using the decision tree correlated with the likelihood of positive test results, suggesting that clinicians appropriately used the questions in the decision tree algorithm.

Assessing comfort level with pediatric skin specimens among dermatopathologists and pediatric pathologists: A national cross-sectional survey.

BACKGROUND: Cutaneous histopathologic diagnoses in children often differ from those in adults. Depending on practice setting, these specimens may be evaluated by dermatopathologists or pediatric pathologists. We sought to determine whether comfort level with pediatric dermatopathology is associated with prior training, pediatric dermatopathology exposure during fellowship, career duration, or specimen subtype. METHODS: We surveyed dermatopathologists and pediatric pathologists practicing in the United States. Training and practice variables were evaluated by multivariable regression for association with comfort level. RESULTS: Of the 156 respondents, 72% were dermatopathologists (response rate 11.6%) and 28% were pediatric pathologists (response rate 9.3%). Dermatopathologists reported higher comfort overall (P < .001); this was also true for inflammatory dermatoses and melanocytic neoplasms (P < .001). Thirty-four percent and 75% of dermatopathologists and pediatric pathologists, respectively, reported lower comfort with pediatric skin specimens than their usual cases. Pediatric pathologists were 28% more likely to refer these cases to colleagues. Among dermatopathologists, dermatology-trained were more comfortable than pathology-trained colleagues interpreting inflammatory dermatoses (P < .001). CONCLUSIONS: Pathologists' comfort with pediatric dermatopathology varied significantly based upon prior training, career duration, and specimen subtype. These results suggest opportunities for improving education in this domain.

Basic Histopathologic Assessment of Germ Cell Tumors for Clinic and Research.

This chapter introduces the macroscopic and light microscopic features of testicular germ cell tumors (GCT) commonly encountered in clinical practice. Accurate diagnosis of these histologically diverse neoplasms is essential not only for clinical management but also for serving as the basis for interpretation of research findings. We will focus on general histopathologic concepts and discuss the use of immunohistochemistry (IHC) as an aid to the diagnosis.

Functional Assessment of Lipoyltransferase-1 Deficiency in Cells, Mice, and Humans.

Inborn errors of metabolism (IEMs) link metabolic defects to human phenotypes. Modern genomics has accelerated IEM discovery, but assessing the impact of genomic variants is still challenging. Here, we integrate genomics and metabolomics to identify a cause of lactic acidosis and epilepsy. The proband is a compound heterozygote for variants in LIPT1, which encodes the lipoyltransferase required for 2-ketoacid dehydrogenase (2KDH) function. Metabolomics reveals abnormalities in lipids, amino acids, and 2-hydroxyglutarate consistent with loss of multiple 2KDHs. Homozygous knockin of a LIPT1 mutation reduces 2KDH lipoylation in utero and results in embryonic demise. In patient fibroblasts, defective 2KDH lipoylation and function are corrected by wild-type, but not mutant, LIPT1 alleles. Isotope tracing reveals that LIPT1 supports lipogenesis and balances oxidative and reductive glutamine metabolism. Altogether, the data extend the role of LIPT1 in metabolic regulation and demonstrate how integrating genomics and metabolomics can uncover broader aspects of IEM pathophysiology.

Viable and necrotic tumor assessment from whole slide images of osteosarcoma using machine-learning and deep-learning models.

Pathological estimation of tumor necrosis after chemotherapy is essential for patients with osteosarcoma. This study reports the first fully automated tool to assess viable and necrotic tumor in osteosarcoma, employing advances in histopathology digitization and automated learning. We selected 40 digitized whole slide images representing the heterogeneity of osteosarcoma and chemotherapy response. With the goal of labeling the diverse regions of the digitized tissue into viable tumor, necrotic tumor, and non-tumor, we trained 13 machine-learning models and selected the top performing one (a Support Vector Machine) based on reported accuracy. We also developed a deep-learning architecture and trained it on the same data set. We computed the receiver-operator characteristic for discrimination of non-tumor from tumor followed by conditional discrimination of necrotic from viable tumor and found our models performing exceptionally well. We then used the trained models to identify regions of interest on image-tiles generated from test whole slide images. The classification output is visualized as a tumor-prediction map, displaying the extent of viable and necrotic tumor in the slide image. Thus, we lay the foundation for a complete tumor assessment pipeline from original histology images to tumor-prediction map generation. The proposed pipeline can also be adopted for other types of tumor.