Development of a natural language processing algorithm for the detection of spinal metastasis based on magnetic resonance imaging reports.

Background: Metastasis to the spinal column is a common complication of malignancy, potentially causing pain and neurologic injury. An automated system to identify and refer patients with spinal metastases can help overcome barriers to timely treatment. We describe the training, optimization and validation of a natural language processing algorithm to identify the presence of vertebral metastasis and metastatic epidural cord compression (MECC) from radiology reports of spinal MRIs.

Post-acute and Chronic Kidney Function Outcomes of COVID-19 in Children and Adolescents: An EHR Cohort Study from the RECOVER Initiative.

We investigated the risks of post-acute and chronic adverse kidney outcomes of SARS-CoV-2 infection in the pediatric population via a retrospective cohort study using data from the RECOVER program. We included 1,864,637 children and adolescents under 21 from 19 children's hospitals and health institutions in the US with at least six months of follow-up time between March 2020 and May 2023. We divided the patients into three strata: patients with pre-existing chronic kidney disease (CKD), patients with acute kidney injury (AKI) during the acute phase (within 28 days) of SARS-CoV-2 infection, and patients without pre-existing CKD or AKI.

Pediatric Gastrointestinal Outcomes During the Post-Acute Phase of COVID-19: Findings from RECOVER Initiative from 29 Hospitals in the US.

Importance: The profile of gastrointestinal (GI) outcomes that may affect children in post-acute and chronic phases of COVID-19 remains unclear.

Objective: To investigate the risks of GI symptoms and disorders during the post-acute phase (28 days to 179 days after SARS-CoV-2 infection) and the chronic phase (180 days to 729 days after SARS-CoV-2 infection) in the pediatric population.

Design: We used a retrospective cohort design from March 2020 to Sept 2023.

Long short-term memory model identifies ARDS and in-hospital mortality in both non-COVID-19 and COVID-19 cohort.

Objective: To identify the risk of acute respiratory distress syndrome (ARDS) and in-hospital mortality using long short-term memory (LSTM) framework in a mechanically ventilated (MV) non-COVID-19 cohort and a COVID-19 cohort.

Methods: We included MV ICU patients between 2017 and 2018 and reviewed patient records for ARDS and death. Using active learning, we enriched this cohort with MV patients from 2016 to 2019 (MV non-COVID-19, n=3905).

Incidence, characteristics, risk factors and outcomes of diabetic ketoacidosis in COVID-19 patients: Comparison with influenza and pre-pandemic data.

Aims: This study characterized incidence, patient profiles, risk factors and outcomes of in-hospital diabetic ketoacidosis (DKA) in patients with COVID-19 compared with influenza and pre-pandemic data.

Methods: This study consisted of 13 383 hospitalized patients with COVID-19 (March 2020-July 2022), 19 165 hospitalized patients with influenza (January 2018-July 2022) and 35 000 randomly sampled hospitalized pre-pandemic patients (January 2017-December 2019) in Montefiore Health System, Bronx, NY, USA. Primary outcomes were incidence of in-hospital DKA, in-hospital mortality, and insulin use at 3 and 6 months post-infection.

Characteristics of COVID-19 patients with multiorgan injury across the pandemic in a large academic health system in the Bronx, New York.

Purpose: To investigate the evolution of COVID-19 patient characteristics and multiorgan injury across the pandemic.

Methods: This retrospective cohort study consisted of 40,387 individuals tested positive for SARS-CoV-2 in the Montefiore Health System in Bronx, NY, between March 2020 and February 2022, of which 11,306 were hospitalized. Creatinine, troponin, and alanine aminotransferase were used to define acute kidney injury (AKI), acute cardiac injury (ACI) and acute liver injury, respectively.

Development and validation of a machine learning ASA-score to identify candidates for comprehensive preoperative screening and risk stratification.

Objective: The ASA physical status (ASA-PS) is determined by an anesthesia provider or surgeon to communicate co-morbidities relevant to perioperative risk. Assigning an ASA-PS is a clinical decision and there is substantial provider-dependent variability. We developed and externally validated a machine learning-derived algorithm to determine ASA-PS (ML-PS) based on data available in the medical record.

Long-term outcomes of COVID-19 survivors with hospital AKI: association with time to recovery from AKI.

Background: Although coronavirus disease 2019 (COVID-19) patients who develop in-hospital acute kidney injury (AKI) have worse short-term outcomes, their long-term outcomes have not been fully characterized. We investigated 90-day and 1-year outcomes after hospital AKI grouped by time to recovery from AKI.

Methods: This study consisted of 3296 COVID-19 patients with hospital AKI stratified by early recovery (<48 hours), delayed recovery (2-7 days) and prolonged recovery (>7-90 days).

Comparison of the effects of sugammadex versus neostigmine for reversal of neuromuscular block on hospital costs of care.

Background: Sugammadex reversal of neuromuscular block facilitates recovery of neuromuscular function after surgery, but the drug is expensive. We evaluated the effects of sugammadex on hospital costs of care.

Methods: We analysed 79 474 adult surgical patients who received neuromuscular blocking agents and reversal from two academic healthcare networks between 2016 and 2021 to calculate differences in direct costs.

OpenFL: the open federated learning library.

Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) and deep learning (DL) projects without sharing sensitive data, such as patient records, financial data, or classified secrets.Open federated learning (OpenFL) framework is an open-source python-based tool for training ML/DL algorithms using the data-private collaborative learning paradigm of FL, irrespective of the use case. OpenFL works with training pipelines built with both TensorFlow and PyTorch, and can be easily extended to other ML and DL frameworks.

Improving Adjuvant Liver-Directed Treatment Recommendations for Unresectable Hepatocellular Carcinoma: An Artificial Intelligence-Based Decision-Making Tool.

Purpose: Liver-directed therapy after transarterial chemoembolization (TACE) can lead to improvement in survival for selected patients with unresectable hepatocellular carcinoma (HCC). However, there is uncertainty in the appropriate application and modality of therapy in current clinical practice guidelines. The aim of this study was to develop a proof-of-concept, machine learning (ML) model for treatment recommendation in patients previously treated with TACE and select patients who might benefit from additional treatment with combination stereotactic body radiotherapy (SBRT) or radiofrequency ablation (RFA).

Epidemiology of Organ Failure Before and During COVID-19 Pandemic Surge Conditions.

Background: Understanding the distribution of organ failure before and during the COVID-19 pandemic surge can provide a deeper understanding of how the pandemic strained health care systems and affected outcomes.

Objective: To assess the distribution of organ failure in 3 New York City hospitals during the COVID-19 pandemic.

Methods: A retrospective cohort study of adult admissions across hospitals from February 1, 2020, through May 31, 2020, was conducted.

Central Venous Catheter Salvage in Ambulatory Central Line-Associated Bloodstream Infections.

Background: Guidelines for treatment of central line-associated bloodstream infection (CLABSI) recommend removing central venous catheters (CVCs) in many cases. Clinicians must balance these recommendations with the difficulty of obtaining alternate access and subjecting patients to additional procedures. In this study, we evaluated CVC salvage in pediatric patients with ambulatory CLABSI and associated risk factors for treatment failure.

Clinical characteristics of the first and second COVID-19 waves in the Bronx, New York: A retrospective cohort study.

Background: There is limited clinical patient data comparing the first and second waves of the coronavirus disease 2019 (COVID-19) in the United States and the effects of a COVID-19 resurgence on different age, racial and ethnic groups. We compared the first and second COVID-19 waves in the Bronx, New York, among a racially and ethnically diverse population.

Methods: Patients in this retrospective cohort study were included if they had a laboratory-confirmed SARS-CoV-2 infection by a real-time PCR test of a nasopharyngeal swab specimen detected between March 11, 2020, and January 21, 2021.

Pediatric surgical site infection (SSI) following ambulatory surgery: Incidence, risk factors and patient outcomes.

Background: Inpatient surgical site infections (SSIs) cause morbidity in children. The SSI rate among pediatric ambulatory surgery patients is less clear. To fill this gap, we conducted a multiple-institution, retrospective epidemiologic study to identify incidence, risk factors, and outcomes.

Pediatric Ambulatory Central Line-Associated Bloodstream Infections.

Background: Inpatient pediatric central line-associated bloodstream infections (CLABSIs) cause morbidity and increased health care use. Minimal information exists for ambulatory CLABSIs despite ambulatory central line (CL) use in children. In this study, we identified ambulatory pediatric CLABSI incidence density, risk factors, and outcomes.

Costs of ambulatory pediatric healthcare-associated infections: Central-line-associated bloodstream infection (CLABSIs), catheter-associated urinary tract infection (CAUTIs), and surgical site infections (SSIs).

Objective: Ambulatory healthcare-associated infections (HAIs) occur frequently in children and are associated with morbidity. Less is known about ambulatory HAI costs. This study estimated additional costs associated with pediatric ambulatory central-line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTI), and surgical site infections (SSIs) following ambulatory surgery.

Pediatric ambulatory catheter-associated urinary tract infections (CAUTIs): Incidence, risk factors, and patient outcomes.

Objective: Catheter-associated urinary tract infections (CAUTIs) occur frequently in pediatric inpatients, and they are associated with increased morbidity and cost. Few studies have investigated ambulatory CAUTIs, despite at-risk children utilizing home urinary catheterization. This retrospective cohort and case-control study determined incidence, risk factors, and outcomes of pediatric patients with ambulatory CAUTI.

Multicenter derivation and validation of an early warning score for acute respiratory failure or death in the hospital.

Background: Acute respiratory failure occurs frequently in hospitalized patients and often starts before ICU admission. A risk stratification tool to predict mortality and risk for mechanical ventilation (MV) may allow for earlier evaluation and intervention. We developed and validated an automated electronic health record (EHR)-based model-Accurate Prediction of Prolonged Ventilation (APPROVE)-to identify patients at risk of death or respiratory failure requiring >= 48 h of MV.

Leveraging electronic health records for clinical research.

Electronic health records (EHRs) can be a major tool in the quest to decrease costs and timelines of clinical trial research, generate better evidence for clinical decision making, and advance health care. Over the past decade, EHRs have increasingly offered opportunities to speed up, streamline, and enhance clinical research. EHRs offer a wide range of possible uses in clinical trials, including assisting with prestudy feasibility assessment, patient recruitment, and data capture in care delivery.

Preserving temporal relations in clinical data while maintaining privacy.

Objective: Maintaining patient privacy is a challenge in large-scale observational research. To assist in reducing the risk of identifying study subjects through publicly available data, we introduce a method for obscuring date information for clinical events and patient characteristics.

Methods: The method, which we call Shift and Truncate (SANT), obscures date information to any desired granularity.

Changing the research landscape: the New York City Clinical Data Research Network.

The New York City Clinical Data Research Network (NYC-CDRN), funded by the Patient-Centered Outcomes Research Institute (PCORI), brings together 22 organizations including seven independent health systems to enable patient-centered clinical research, support a national network, and facilitate learning healthcare systems. The NYC-CDRN includes a robust, collaborative governance and organizational infrastructure, which takes advantage of its participants' experience, expertise, and history of collaboration. The technical design will employ an information model to document and manage the collection and transformation of clinical data, local institutional staging areas to transform and validate data, a centralized data processing facility to aggregate and share data, and use of common standards and tools.