Development and validation of an instrument to predict prolonged length of stay in the postanesthesia care unit following ambulatory surgery.

PURPOSE: We sought to develop and validate an Anticipated Surveillance Requirement Prediction Instrument (ASRI) for prediction of prolonged postanesthesia care unit length of stay (PACU-LOS, more than four hours) after ambulatory surgery. METHODS: We analyzed hospital registry data from patients who received anesthesia care in ambulatory surgery centres (ASCs) of university-affiliated hospital networks in New York, USA (development and internal validation cohort [n = 183,711]) and Massachusetts, USA (validation cohort [n = 148,105]). We used stepwise backwards elimination to create ASRI. RESULTS: The model showed discriminatory ability in the development, internal, and external validation cohorts with areas under the receiver operating characteristic curve of 0.82 (95% confidence interval [CI], 0.82 to 0.83), 0.82 (95% CI, 0.81 to 0.83), and 0.80 (95% CI, 0.79 to 0.80), respectively. In cases started in the afternoon, ASRI scores ≥ 43 had a total predicted risk for PACU stay past 8 p.m. of 32% (95% CI, 31.1 to 33.3) vs 8% (95% CI, 7.9 to 8.5) compared with low score values (P-for-interaction < 0.001), which translated to a higher direct PACU cost of care of USD 207 (95% CI, 194 to 2,019; model estimate, 1.68; 95% CI, 1.64 to 1.73; P < 0.001) The effects of using the ASRI score on PACU use efficiency were greater in a free-standing ASC with no limitations on PACU bed availability. CONCLUSION: We developed and validated a preoperative prediction tool for prolonged PACU-LOS after ambulatory surgery that can be used to guide scheduling in ambulatory surgery to optimize PACU use during normal work hours, particularly in settings without limitation of PACU bed availability.

RéSUMé: OBJECTIF: Nous avons cherché à mettre au point et à valider un Instrument de prédiction anticipée des besoins de surveillance pour anticiper toute prolongation de la durée de séjour en salle de réveil (plus de quatre heures) après chirurgie ambulatoire. MéTHODE: Nous avons analysé les données enregistrées dans le registre de l'hôpital des patient·es qui ont reçu des soins d'anesthésie dans des centres de chirurgie ambulatoire (CCA) des réseaux hospitaliers affiliés à une université à New York, aux États-Unis (cohorte de développement et de validation interne [n = 183 711]) et au Massachusetts, États-Unis (cohorte de validation [n = 148 105]). Nous avons utilisé un procédé d'élimination progressive régressive pour créer notre instrument de prédiction. RéSULTATS: Le modèle a montré une capacité discriminatoire dans les cohortes de développement, de validation interne et de validation externe, avec des surfaces sous la courbe de fonction d'efficacité de l'opérateur (ROC) de 0,82 (intervalle de confiance [IC] à 95 %, 0,82 à 0,83), 0,82 (IC 95 %, 0,81 à 0,83), et 0,80 (IC 95 %, 0,79 à 0,80), respectivement. Dans les cas commencés en après-midi, les scores sur notre instrument de prédiction ≥ 43 montraient un risque total prédit de séjour en salle de réveil après 20 h de 32 % (IC 95 %, 31,1 à 33,3) vs 8 % (IC 95 %, 7,9 à 8,5) comparativement aux valeurs de score faibles (P-pour-interaction < 0,001), ce qui s'est traduit par une augmentation de 207 USD du coût direct des soins en salle de réveil (IC 95 %, 194 à 2019; estimation du modèle, 1,68; IC 95 %, 1,64 à 1,73; P < 0,001). Les effets de l'utilisation du score de notre instrument de prédiction sur l'efficacité d'utilisation de la salle de réveil étaient plus importants dans un CCA autonome sans limitation dans la disponibilité des lits en salle de réveil. CONCLUSION: Nous avons mis au point et validé un outil de prédiction préopératoire de la prolongation de la durée de séjour en salle de réveil après une chirurgie ambulatoire qui peut être utilisé pour guider la planification en chirurgie ambulatoire afin d'optimiser l'utilisation de la salle de réveil pendant les heures normales de travail, en particulier dans les milieux sans limitation de disponibilité des lits en salle de réveil.

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). We collected a second validation cohort of hospitalised patients with COVID-19 in 2020 (COVID+, n=5672). We trained an LSTM model using 132 structured features on the MV non-COVID-19 training cohort and validated on the MV non-COVID-19 validation and COVID-19 cohorts. RESULTS: Applying LSTM (model score 0.9) on the MV non-COVID-19 validation cohort had a sensitivity of 86% and specificity of 57%. The model identified the risk of ARDS 10 hours before ARDS and 9.4 days before death. The sensitivity (70%) and specificity (84%) of the model on the COVID-19 cohort are lower than MV non-COVID-19 cohort. For the COVID-19 + cohort and MV COVID-19 + patients, the model identified the risk of in-hospital mortality 2.4 days and 1.54 days before death, respectively. DISCUSSION: Our LSTM algorithm accurately and timely identified the risk of ARDS or death in MV non-COVID-19 and COVID+ patients. By alerting the risk of ARDS or death, we can improve the implementation of evidence-based ARDS management and facilitate goals-of-care discussions in high-risk patients. CONCLUSION: Using the LSTM algorithm in hospitalised patients identifies the risk of ARDS or death.

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. Risk factors for developing DKA were identified. RESULTS: The overall incidence of DKA in patients with COVID-19 and influenza, and pre-pandemic were 2.1%, 1.4% and 0.5%, respectively (p < .05 pairwise). Patients with COVID-19 with DKA had worse acute outcomes (p < .05) and higher incidence of new insulin treatment 3 and 6 months post-infection compared with patients with influenza with DKA (p < .05). The incidence of DKA in patients with COVID-19 was highest among patients with type 1 diabetes (12.8%), followed by patients with insulin-dependent type 2 diabetes (T2D; 5.2%), non-insulin dependent T2D (2.3%) and, lastly, patients without T2D (1.3%). Patients with COVID-19 with DKA had worse disease severity and higher mortality [odds ratio = 6.178 (4.428-8.590), p < .0001] compared with those without DKA. Type 1 diabetes, steroid therapy for COVID-19, COVID-19 status, black race and male gender were associated with increased risk of DKA. CONCLUSIONS: The incidence of DKA was higher in COVID-19 cohort compared to the influenza and pre-pandemic cohort. Patients with COVID-19 with DKA had worse outcomes compared with those without. Many COVID-19 survivors who developed DKA during hospitalization became insulin dependent. Identification of risk factors for DKA and new insulin-dependency could enable careful monitoring and timely intervention.

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. Demographics, comorbidities, emergency department visits, hospitalization, intensive care utilization, and mortality were analyzed across the pandemic. Results: COVID-19 positive cases, emergency department visits, hospitalization and mortality rate showed four distinct waves with a large first wave in April 2020, two small (Alpha and Delta) waves, and a large Omicron wave in December 2021. Omicron was more infectious but less lethal (p = 0.05). Among hospitalized COVID-19 patients, age decreased (p = 0.014), female percentage increased (p = 0.023), Hispanic (p = 0.028) and non-Hispanic Black (p = 0.05) percentages decreased, and patients with pre-existing diabetes (p = 0.002) and hypertension (p = 0.04) decreased across the pandemic. More than half (53.1%) of hospitalized patients had major organ injury. Patients with AKI, ACI and its combinations were older, more likely males, had more comorbidities, and consisted more of non-Hispanic Black and Hispanic patients (p = 0.005). Patients with AKI and its combinations had 4-9 times higher adjusted risk of mortality than those without. Conclusions: There were shifts in demographics toward younger age and proportionally more females with COVID-19 across the pandemic. While the overall trend showed improved clinical outcomes, a substantial number of COVID-19 patients developed multi-organ injuries over time. These findings could bring awareness to at-risk patients for long-term organ injuries and help to better inform public policy and outreach initiatives.

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. DESIGN: Retrospective multicenter hospital registry study. SETTING: University-affiliated hospital networks. PATIENTS: Patients who received anesthesia at Beth Israel Deaconess Medical Center (Boston, MA, training [n = 361,602] and internal validation cohorts [n = 90,400]) and Montefiore Medical Center (Bronx, NY, external validation cohort [n = 254,412]). MEASUREMENTS: The ML-PS was created using a supervised random forest model with 35 preoperatively available variables. Its predictive ability for 30-day mortality, postoperative ICU admission, and adverse discharge were determined by logistic regression. MAIN RESULTS: The anesthesiologist ASA-PS and ML-PS were in agreement in 57.2% of the cases (moderate inter-rater agreement). Compared with anesthesiologist rating, ML-PS assigned more patients into extreme ASA-PS (I and IV), (p < 0.01), and less patients in ASA II and III (p < 0.01). ML-PS and anesthesiologist ASA-PS had excellent predictive values for 30-day mortality, and good predictive values for postoperative ICU admission and adverse discharge. Among the 3594 patients who died within 30 days after surgery, net reclassification improvement analysis revealed that using the ML-PS, 1281 (35.6%) patients were reclassified into the higher clinical risk category compared with anesthesiologist rating. However, in a subgroup of multiple co-morbidity patients, anesthesiologist ASA-PS had a better predictive accuracy than ML-PS. CONCLUSIONS: We created and validated a machine learning physical status based on preoperatively available data. The ability to identify patients at high risk early in the preoperative process independent of the provider's decision is a part of the process we use to standardize the stratified preoperative evaluation of patients scheduled for ambulatory surgery.

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). Demographics, comorbidities and laboratory values were obtained at admission and up to the 1-year follow-up. The incidence of major adverse cardiovascular events (MACE) and major adverse kidney events (MAKE), rehospitalization, recurrent AKI and new-onset chronic kidney disease (CKD) were obtained 90-days after COVID-19 discharge. RESULTS: The incidence of hospital AKI was 28.6%. Of the COVID-19 patients with AKI, 58.0% experienced early recovery, 14.8% delayed recovery and 27.1% prolonged recovery. Patients with a longer AKI recovery time had a higher prevalence of CKD (P < .05) and were more likely to need invasive mechanical ventilation (P < .001) and to die (P < .001). Many COVID-19 patients developed MAKE, recurrent AKI and new-onset CKD within 90 days, and these incidences were higher in the prolonged recovery group (P < .05). The incidence of MACE peaked 20-40 days postdischarge, whereas MAKE peaked 80-90 days postdischarge. Logistic regression models predicted 90-day MACE and MAKE with 82.4 ± 1.6% and 79.6 ± 2.3% accuracy, respectively. CONCLUSION: COVID-19 survivors who developed hospital AKI are at high risk for adverse cardiovascular and kidney outcomes, especially those with longer AKI recovery times and those with a history of CKD. These patients may require long-term follow-up for cardiac and kidney complications.

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. We matched our data with data from the Healthcare Cost and Utilization Project-National Inpatient Sample (HCUP-NIS) to calculate differences in total costs in US dollars. Perioperative risk profiles were defined based on ASA physical status and admission status (ambulatory surgery vs hospitalisation). RESULTS: Based on our registry data analysis, administration of sugammadex vs neostigmine was associated with lower direct costs (-1.3% lower costs; 95% confidence interval [CI], -0.5 to -2.2%; P=0.002). In the HCUP-NIS matched cohort, sugammadex use was associated with US$232 lower total costs (95% CI, -US$376 to -US$88; P=0.002). Subgroup analysis revealed that sugammadex was associated with US$1042 lower total costs (95% CI, -US$1198 to -US$884; P<0.001) in patients with lower risk. In contrast, sugammadex was associated with US$620 higher total costs (95% CI, US$377 to US$865; P<0.001) in patients with a higher risk (American Society of Anesthesiologists physical status ≥3 and preoperative hospitalisation). CONCLUSIONS: The effects of using sugammadex on costs of care depend on patient risk, defined based on comorbidities and admission status. We observed lower costs of care in patients with lower risk and higher costs of care in hospitalised surgical patients with severe comorbidities.

OpenFL: the open federated learning library.

Objective.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.Approach.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.Main results.In this manuscript, we present OpenFL and summarize its motivation and development characteristics, with the intention of facilitating its application to existing ML/DL model training in a production environment. We further provide recommendations to secure a federation using trusted execution environments to ensure explicit model security and integrity, as well as maintain data confidentiality. Finally, we describe the first real-world healthcare federations that use the OpenFL library, and highlight how it can be applied to other non-healthcare use cases.Significance.The OpenFL library is designed for real world scalability, trusted execution, and also prioritizes easy migration of centralized ML models into a federated training pipeline. Although OpenFL's initial use case was in healthcare, it is applicable beyond this domain and is now reaching wider adoption both in research and production settings. The tool is open-sourced atgithub.com/intel/openfl.

Incidence and predictors of case cancellation within 24 h in patients scheduled for elective surgical procedures.

OBJECTIVE: Avoidable case cancellations within 24 h reduce operating room (OR) efficiency, add unnecessary costs, and may have physical and emotional consequences for patients and their families. We developed and validated a prediction tool that can be used to guide same day case cancellation reduction initiatives. DESIGN: Retrospective hospital registry study. SETTING: University-affiliated hospitals network (NY, USA). PATIENTS: 246,612 (1/2016-6/2021) and 58,662 (7/2021-6/2022) scheduled elective procedures were included in the development and validation cohort. MEASUREMENTS: Case cancellation within 24 h was defined as cancelling a surgical procedure within 24 h of the scheduled date and time. Our candidate predictors were defined a priori and included patient-, procedural-, and appointment-related factors. We created a prediction tool using backward stepwise logistic regression to predict case cancellation within 24 h. The model was subsequently recalibrated and validated in a cohort of patients who were recently scheduled for surgery. MAIN RESULTS: 8.6% and 8.7% scheduled procedures were cancelled within 24 h of the intended procedure in the development and validation cohort, respectively. The final weighted score contains 29 predictors. A cutoff value of 15 score points predicted a 10.3% case cancellation rate with a negative predictive value of 0.96, and a positive predictive value of 0.21. The prediction model showed good discrimination in the development and validation cohort with an area under the receiver operating characteristic curve (AUC) of 0.79 (95% confidence interval 0.79-0. 80) and an AUC of 0.73 (95% confidence interval 0.72-0.73), respectively. CONCLUSIONS: We present a validated preoperative prediction tool for case cancellation within 24 h of surgery. We utilize the instrument in our institution to identify patients with high risk of case cancellation. We describe a process for recalibration such that other institutions can also use the score to guide same day case cancellation reduction initiatives.

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). METHODS: This retrospective observational study was based on data from an urban, academic hospital system selecting for patients diagnosed with stage I-III HCC from January 1, 2008, to December 31, 2018, treated with TACE, followed by adjuvant RFA, SBRT, or no additional liver-directed modality. A feedforward, ML ensemble model provided a treatment recommendation on the basis of pairwise assessments evaluating each potential treatment option and estimated benefit in survival. RESULTS: Two hundred thirty-seven patients met inclusion criteria, of whom 54 (23%) and 49 (21%) received combination of TACE and SBRT or TACE and RFA, respectively. The ML model suggested a different consolidative modality in 32.7% of cases among patients who had previously received combination treatment. Patients treated in concordance with model recommendations had significant improvement in progression-free survival (hazard ratio 0.5; P = .007). The most important features for model prediction were cause of cirrhosis, stage of disease, and albumin-bilirubin grade (a measure of liver function). CONCLUSION: In this proof-of-concept study, an ensemble ML model was able to provide treatment recommendations for HCC who had undergone prior TACE. Additional treatment in line with model recommendations was associated with significant improvement in progression-free survival, suggesting a potential benefit for ML-guided medical decision making.

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. The cohort was stratified into those admitted before March 17, 2020 (prepandemic) and those admitted on or after that date (SARS-CoV-2-positive and non-SARS-CoV-2). Sequential Organ Failure Assessment scores were computed every 2 hours for each admission. RESULTS: A total of 1 794 975 scores were computed for 20 704 admissions. Before and during the pandemic, renal failure was the most common type of organ failure at admission and respiratory failure was the most common type of hospital-onset organ failure. The SARS-CoV-2-positive group showed a 231% increase in respiratory failure compared with the prepandemic group. More than 65% of hospital-onset organ failure in the prepandemic group and 83% of hospital-onset respiratory failure in the SARS-CoV-2-positive group occurred outside intensive care units. The SARS-CoV-2-positive group showed a 341% increase in multiorgan failure compared with the prepandemic group. Compared with the prepandemic and non-SARS-CoV-2 patients, SARS-CoV-2-positive patients had significantly higher mortality for the same admission and maximum organ failure score. CONCLUSION: Most hospital-onset organ failure began outside intensive care units, with a marked increase in multiorgan failure during pandemic surge conditions and greater hospital mortality for the severity of organ failure.

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. METHODS: We identified patients aged <22 years with ambulatory visits between October 2010 and September 2015 via electronic queries at 3 medical centers. We performed sample chart reviews to confirm ambulatory surgery and adjudicate SSIs. Weighted Poisson incidence rates were calculated. Separately, we used case-control methodology using multivariate backward logistical regression to assess risk-factor association with SSI. RESULTS: In total, 65,056 patients were identified by queries, and we performed complete chart reviews for 13,795 patients; we identified 45 SSIs following ambulatory surgery. The weighted SSI incidence following pediatric ambulatory surgery was 2.00 SSI per 1,000 ambulatory surgeries (95% confidence interval [CI], 1.37-3.00). Integumentary surgeries had the highest weighted SSI incidence, 3.24 per 1,000 ambulatory surgeries (95% CI, 0.32-12). The following variables carried significantly increased odds of infection: clean contaminated or contaminated wound class compared to clean (odds ratio [OR], 9.8; 95% CI, 2.0-48), other insurance type compared to private (OR, 4.0; 95% CI, 1.6-9.8), and surgery on weekend day compared to weekday (OR, 30; 95% CI, 2.9-315). Of the 45 instances of SSI following pediatric ambulatory surgery, 40% of patients were admitted to the hospital and 36% required a new operative procedure or bedside incision and drainage. CONCLUSIONS: Our findings suggest that morbidity is associated with SSI following ambulatory surgery in children, and we also identified possible targets for intervention.

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. METHODS: This study was a secondary analysis of 466 ambulatory CLABSIs in patients <22 years old who presented to 5 pediatric medical centers from 2010 to 2015. We defined attempted CVC salvage as a CVC left in place ≥3 days after a positive blood culture result. Salvage failure was removal of the CVC ≥3 days after CLABSI. Successful salvage was treatment of CLABSI without removal of the CVC. Bivariate and multivariable logistic regression analyses were used to test associations between risk factors and attempted and successful salvage. RESULTS: A total of 460 ambulatory CLABSIs were included in our analysis. CVC salvage was attempted in 379 (82.3%) cases. Underlying diagnosis, CVC type, number of lumens, and absence of candidemia were associated with attempted salvage. Salvage was successful in 287 (75.7%) attempted cases. Underlying diagnosis, CVC type, number of lumens, and absence of candidemia were associated with successful salvage. In patients with malignancy, neutropenia within 30 days before CLABSI was significantly associated with both attempted salvage and successful salvage. CONCLUSIONS: CVC salvage was often attempted and was frequently successful in ambulatory pediatric patients presenting with CLABSI.

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. Main outcome measures were critical care, in-hospital acquired disease and death. Patient demographics, comorbidities, vitals, and laboratory values were also collected. FINDINGS: A total of 122,983 individuals were tested for SARS-CoV-2 infection, of which 12,659 tested positive. The second wave was characterized by a younger demographic, fewer comorbidities, less extreme laboratory values at presentation, and lower risk of adverse outcomes, including in-hospital mortality (adj. OR = 0·23, 99·5% CI = 0·17 to 0·30), hospitalization (adj. OR = 0·65, 99·5% CI = 0·58 to 0·74), invasive mechanical ventilation (adj. OR = 0·70, 99·5% CI = 0·56 to 0·89), acute kidney injury (adj. OR = 0·62, 99·5% CI = 0·54 to 0·71), and length of stay (adj. OR = 0·71, 99·5% CI = 0·60 to 0·85), with Black and Hispanic patients demonstrating most improvement in clinical outcomes. INTERPRETATION: The second COVID-19 wave in the Bronx exhibits improved clinical outcomes compared to the first wave across all age, racial, and ethnic groups, with minority groups showing more improvement, which is encouraging news in the battle against health disparities.

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. METHODS: Retrospective cohort with nested case-control study at 5 sites from 2010 through 2015. Electronic queries were used to identify potential cases on the basis of administrative and laboratory data. Chart review was used to confirm ambulatory CL use and adjudicated CLABSIs. Bivariate followed by multivariable backward logistic regression was used to identify ambulatory CLABSI risk factors. RESULTS: Queries identified 4600 potentially at-risk children; 1658 (36%) had ambulatory CLs. In total, 247 (15%) patients experienced 466 ambulatory CLABSIs with an incidence density of 0.97 CLABSIs per 1000 CL days. Incidence density was highest among patients with tunneled externalized catheters versus peripherally inserted central catheters and totally implanted devices: 2.58 CLABSIs per 1000 CL days versus 1.46 vs 0.23, respectively (P < .001). In a multivariable model, clinic visit (odds ratio [OR] 2.8; 95% confidence interval [CI]: 1.4-5.5) and low albumin (OR 2.3; 95% CI: 1.2-4.3) were positively associated with CLABSI, and prophylactic antimicrobial agents for underlying conditions within the preceding 30 days (OR 0.22; 95% CI: 0.12-0.40) and operating room CL placement (OR 0.36; 95% CI: 0.16-0.79) were inversely associated with CLABSI. A total of 396 patients (85%) were hospitalized because of ambulatory CLABSI with an 8-day median length of stay (interquartile range 5-13). CONCLUSIONS: Ambulatory pediatric CLABSI incidence density is appreciable and associated with health care use. CL type, patients with low albumin, prophylactic antimicrobial agents, and placement setting may be targets for reduction efforts.

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. DESIGN: Retrospective case-control study. SETTING: Four academic medical centers. PATIENTS: Children aged 0-22 years seen between 2010 and 2015 and at risk for HAI as identified by electronic queries. METHODS: Chart review adjudicated HAIs. Charges were obtained for patients with HAIs and matched controls 30 days before HAI, on the day of, and 30 days after HAI. Charges were converted to costs and 2015 USD. Mixed-effects linear regression was used to estimate the difference-in-differences of HAI case versus control costs in 2 models: unrecorded charge values considered missing and a sensitivity analysis with unrecorded charge considered $0. RESULTS: Our search identified 177 patients with ambulatory CLABSIs, 53 with ambulatory CAUTIs, and 26 with SSIs following ambulatory surgery who were matched with 382, 110, and 75 controls, respectively. Additional cost associated with an ambulatory CLABSI was $5,684 (95% confidence interval [CI], $1,005-$10,362) and $6,502 (95% CI, $2,261-$10,744) in the 2 models; cost associated with a CAUTI was $6,660 (95% CI, $1,055, $12,145) and $2,661 (95% CI, -$431 to $5,753); cost associated with an SSI following ambulatory surgery at 1 institution only was $6,370 (95% CI, $4,022-$8,719). CONCLUSIONS: Ambulatory HAI in pediatric patients are associated with significant additional costs. Further work is needed to reduce ambulatory HAIs.

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. DESIGN: Broad electronic queries identified potential patients with ambulatory urinary catheters, and direct chart review confirmed catheters and adjudicated whether ambulatory CAUTI occurred. CAUTI definitions included clean intermittent catheterization (CIC). Our matched case-control analysis assessed risk factors. SETTING: Five urban, academic medical centers, part of the New York City Clinical Data Research Network. PATIENTS: Potential patients were age <22 years who were seen between October 2010 and September 2015. RESULTS: In total, 3,598 eligible patients were identified; 359 of these used ambulatory catheterization (representing186,616 ambulatory catheter days). Of these, 63 patients (18%) experienced 95 ambulatory CAUTIs. The overall ambulatory CAUTI incidence was 0.51 infections per 1,000 catheter days (1.35 for indwelling catheters and 0.47 for CIC; incidence rate ratio, 2.88). Patients with nonprivate medical insurance (odds ratio, 2.5; 95% confidence interval, 1.1-6.3) were significantly more likely to have ambulatory CAUTIs in bivariate models but not multivariable models. Also, 45% of ambulatory CAUTI resulted in hospitalization (median duration, 3 days); 5% resulted in intensive care admission; 47% underwent imaging; and 88% were treated with antibiotics. CONCLUSIONS: Pediatric ambulatory CAUTIs occur in 18% of patients with catheters; they are associated with morbidity and healthcare utilization. Ambulatory indwelling catheter CAUTI incidence exceeded national inpatient incidence. Future quality improvement research to reduce these harmful infections is warranted.

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. METHODS: This was an observational study of adults admitted to four hospitals in 2013 or a fifth hospital in 2017. Clinical data were extracted from the EHRs. The 2013 patients were randomly split 50:50 into a derivation/validation cohort. The qualifying event was death or intubation leading to MV >= 48 h. Random forest method was used in model derivation. APPROVE was calculated retrospectively whenever data were available in 2013, and prospectively every 4 h after hospital admission in 2017. The Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS) were calculated at the same times as APPROVE. Clinicians were not alerted except for APPROVE in 2017cohort. RESULTS: There were 68,775 admissions in 2013 and 2258 in 2017. APPROVE had an area under the receiver operator curve of 0.87 (95% CI 0.85-0.88) in 2013 and 0.90 (95% CI 0.84-0.95) in 2017, which is significantly better than the MEWS and NEWS in 2013 but similar to the MEWS and NEWS in 2017. At a threshold of > 0.25, APPROVE had similar sensitivity and positive predictive value (PPV) (sensitivity 63% and PPV 21% in 2013 vs 64% and 16%, respectively, in 2017). Compared to APPROVE in 2013, at a threshold to achieve comparable PPV (19% at MEWS > 4 and 22% at NEWS > 6), the MEWS and NEWS had lower sensitivity (16% for MEWS and NEWS). Similarly in 2017, at a comparable sensitivity threshold (64% for APPROVE > 0.25 and 67% for MEWS and NEWS > 4), more patients who triggered an alert developed the event with APPROVE (PPV 16%) while achieving a lower false positive rate (FPR 5%) compared to the MEWS (PPV 7%, FPR 14%) and NEWS (PPV 4%, FPR 25%). CONCLUSIONS: An automated EHR model to identify patients at high risk of MV or death was validated retrospectively and prospectively, and was determined to be feasible for real-time risk identification. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02488174 . Registered on 18 March 2015.

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. To fully appreciate these opportunities, health care stakeholders must come together to face critical challenges in leveraging EHR data, including data quality/completeness, information security, stakeholder engagement, and increasing the scale of research infrastructure and related governance. Leaders from academia, government, industry, and professional societies representing patient, provider, researcher, industry, and regulator perspectives convened the Leveraging EHR for Clinical Research Now! Think Tank in Washington, DC (February 18-19, 2016), to identify barriers to using EHRs in clinical research and to generate potential solutions. Think tank members identified a broad range of issues surrounding the use of EHRs in research and proposed a variety of solutions. Recognizing the challenges, the participants identified the urgent need to look more deeply at previous efforts to use these data, share lessons learned, and develop a multidisciplinary agenda for best practices for using EHRs in clinical research. We report the proceedings from this think tank meeting in the following paper.