The Effects of the COVID-19 Pandemic on Mastectomy Outcomes for Breast Cancer.

BACKGROUND: Single center studies have shown that during the Coronavirus Disease 2019 (COVID-19) pandemic, many patients had surgical procedures postponed or modified. We studied how the pandemic affected the clinical outcomes of breast cancer patients who underwent mastectomies in 2020. METHODS: Using the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) database, we compared clinical variables of 31,123 and 28,680 breast cancer patients who underwent a mastectomy in 2019 and 2020, respectively. Data from 2019 served as the control, and data from 2020 represented the COVID-19 cohort. RESULTS: Fewer surgeries of all kinds were performed in the COVID-19 year than in the control (902,968 vs. 1,076,411). The proportion of mastectomies performed in the COVID-19 cohort was greater than in the control year (3.18% vs. 2.89%, <0.001). More patients presented with ASA level 3 in the COVID-19 year vs. the control (P < .002). Additionally, the proportion of patients with disseminated cancer was lower during the COVID-19 year (P < .001). Average hospital length of stay (P < .001) and time from operation to discharge were shorter in the COVID vs. control cohort (P < .001). Fewer unplanned readmissions were seen in the COVID year (P < .004). CONCLUSION: The ongoing surgical services and mastectomies for breast cancer during the pandemic produced similar clinical outcomes to those seen in 2019. Prioritization of resources for sicker patients and the use of alternative interventions produced similar results for breast cancer patients who underwent a mastectomy in 2020.

The effect of race coefficients on preemptive listing for kidney transplantation.

Background: Race coefficients of estimated glomerular filtration rate (eGFR) formulas may be partially responsible for racial inequality in preemptive listing for kidney transplantation. Methods: We used the Scientific Registry of Transplant Recipients database to evaluate differences in racial distribution of preemptive listing before and after application of the Modification of Diet in Renal Disease (MDRD) and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) race coefficients to all preemptively listed non-Black kidney transplant candidates (eGFR modulation). Odds of preemptive listing were calculated by race, with Black as the reference before and after eGFR modulation. Variables known to influence preemptive listing were included in the model. Results: Among 385 087 kidney-alone transplant candidates from 1 January 2010 to 2 December 2020, 118 329 (30.7%) candidates were identified as preemptively listed (71.7% White, 19% Black, 7.8% Asian, 0.6% multi-racial, 0.6% Native American and 0.3% Pacific Islander). After eGFR modulation, non-Black patients with an eGFR ≥20 mL/min/1.73 m2 were removed. Compared with Black candidates, the adjusted odds of preemptive listing for White candidates decreased from 2.01 [95% confidence interval (95% CI) 1.78-2.26] before eGFR modulation to 1.18 (95% CI 1.0-1.39; P = 0.046) with the MDRD and 1.37 (95% CI 1.18-1.58) with the CKD-EPI equations after adjusting for race coefficients. Conclusions: Removing race coefficients in GFR estimation formulas may result in a more equitable distribution of Black candidates listed earlier on a preemptive basis.

The role of eCIRP in bleomycin-induced pulmonary fibrosis in mice.

OBJECTIVE AND DESIGN: We examined the role of eCIRP in the pathogenesis of bleomycin-induced pulmonary fibrosis (PF). MATERIAL AND METHODS: Publicly available gene expression omnibus datasets were analyzed for the expression of CIRP in lung samples from patients with PF. Wild type (WT) or CIRP-/- mice received daily injections of 10 µg/g bleomycin for 10 days. A subset of bleomycin-injected WT mice was treated with the eCIRP antagonist C23 (8 µg/g/day) from day 10 to day 19. At three weeks, transthoracic echocardiography was performed to measure the degree of pulmonary hypertension, and lung tissues were collected and analyzed for markers of fibrosis. RESULTS: Analysis of the mRNA data of human lung samples showed a significant positive correlation between CIRP and α-smooth muscle actin (α-SMA), an important marker of fibrosis. Moreover, the expression of CIRP was higher in patients with acute exacerbation of PF than in patients with stable PF. CIRP-/- mice showed attenuated induction of α-SMA and collagens (Col1a1, Col3a1), reduced hydroxyproline content, decreased histological fibrosis scores, and improved pulmonary hypertension as compared to WT mice. WT mice treated with C23 also had significant attenuation of the above endpoint measure. CONCLUSIONS: Our study demonstrates that eCIRP plays a key role in promoting the development of PF, and blocking eCIRP with C23 can significantly attenuate this process.

Extracellular CIRP Induces an Inflammatory Phenotype in Pulmonary Fibroblasts via TLR4.

Extracellular cold-inducible RNA-binding protein (eCIRP), a new damage-associated molecular pattern (DAMP), has been recently shown to play a critical role in promoting the development of bleomycin-induced pulmonary fibrosis. Although fibroblast activation is a critical component of the fibrotic process, the direct effects of eCIRP on fibroblasts have never been examined. We studied eCIRP's role in the induction of inflammatory phenotype in pulmonary fibroblasts and its connection to bleomycin-induced pulmonary fibrosis in mice. We found that eCIRP causes the induction of proinflammatory cytokines and differentially expression-related pathways in a TLR4-dependent manner in pulmonary fibroblasts. Our analysis further showed that the accessory pathways MD2 and Myd88 are involved in the induction of inflammatory phenotype. In order to study the connection of the enrichment of these pathways in priming the microenvironment for pulmonary fibrosis, we investigated the gene expression profile of lung tissues from mice subjected to bleomycin-induced pulmonary fibrosis collected at various time points. We found that at day 14, which corresponds to the inflammatory-to-fibrotic transition phase after bleomycin injection, TLR4, MD2, and Myd88 were induced, and the transcriptome was differentially enriched for genes in those pathways. Furthermore, we also found that inflammatory cytokines gene expressions were induced, and the cellular responses to these inflammatory cytokines were differentially enriched on day 14. Overall, our results show that eCIRP induces inflammatory phenotype in pulmonary fibroblasts in a TLR4 dependent manner. This study sheds light on the mechanism by which eCIRP induced inflammatory fibroblasts, contributing to pulmonary fibrosis.

The interplay of DAMPs, TLR4, and proinflammatory cytokines in pulmonary fibrosis.

Pulmonary fibrosis is a chronic debilitating condition characterized by progressive deposition of connective tissue, leading to a steady restriction of lung elasticity, a decline in lung function, and a median survival of 4.5 years. The leading causes of pulmonary fibrosis are inhalation of foreign particles (such as silicosis and pneumoconiosis), infections (such as post COVID-19), autoimmune diseases (such as systemic autoimmune diseases of the connective tissue), and idiopathic pulmonary fibrosis. The therapeutics currently available for pulmonary fibrosis only modestly slow the progression of the disease. This review is centered on the interplay of damage-associated molecular pattern (DAMP) molecules, Toll-like receptor 4 (TLR4), and inflammatory cytokines (such as TNF-α, IL-1ß, and IL-17) as they contribute to the pathogenesis of pulmonary fibrosis, and the possible avenues to develop effective therapeutics that disrupt this interplay.

A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation.

BACKGROUND: Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease. OBJECTIVE: Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department. METHODS: Data were collected from patients with COVID-19 who were admitted to Northwell Health acute care hospitals and were discharged, died, or spent a minimum of 48 hours in the hospital between March 1 and May 11, 2020. Of 11,525 patients, 933 (8.1%) were placed on invasive mechanical ventilation within 48 hours of admission. Variables used by the models included clinical and laboratory data commonly collected in the emergency department. We trained and validated three predictive models (two based on XGBoost and one that used logistic regression) using cross-hospital validation. We compared model performance among all three models as well as an established early warning score (Modified Early Warning Score) using receiver operating characteristic curves, precision-recall curves, and other metrics. RESULTS: The XGBoost model had the highest mean accuracy (0.919; area under the curve=0.77), outperforming the other two models as well as the Modified Early Warning Score. Important predictor variables included the type of oxygen delivery used in the emergency department, patient age, Emergency Severity Index level, respiratory rate, serum lactate, and demographic characteristics. CONCLUSIONS: The XGBoost model had high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19.

Cleaning Up the MESS: Can Machine Learning Be Used to Predict Lower Extremity Amputation after Trauma-Associated Arterial Injury?

BACKGROUND: Thirty years after the Mangled Extremity Severity Score was developed, advances in vascular, trauma, and orthopaedic surgery have rendered the sensitivity of this score obsolete. A significant number of patients receive amputation during subsequent admissions, which are often missed in the analysis of amputation at the index admission. We aimed to identify risk factors for and predict amputation on initial admission or within 30 days of discharge (peritraumatic amputation [PTA]). STUDY DESIGN: The Nationwide Readmission Database for 2016 and 2017 was used in our analysis. Factors associated with PTA were identified. We used XGBoost, random forest, and logistic regression methods to develop a framework for machine learning-based prediction models for PTA. RESULTS: We identified 1,098 adult patients with traumatic lower extremity fracture and arterial injuries; 206 underwent amputation. One hundred and seventy-six patients (85.4%) underwent amputation during the index admission and 30 (14.6%) underwent amputation within a 30-day readmission period. After identifying factors associated with PTA, we constructed machine learning models based on random forest, XGBoost, and logistic regression to predict PTA. We discovered that logistic regression had the most robust predictive ability, with an accuracy of 0.88, sensitivity of 0.47, and specificity of 0.98. We then built on the logistic regression by the NearMiss algorithm, increasing sensitivity to 0.71, but decreasing accuracy to 0.74 and specificity to 0.75. CONCLUSIONS: Machine learning-based prediction models combined with sampling algorithms (such as the NearMiss algorithm in this study), can help identify patients with traumatic arterial injuries at high risk for amputation and guide targeted intervention in the modern age of vascular surgery.

Using machine learning to predict early readmission following esophagectomy.

OBJECTIVE: To establish a machine learning (ML)-based prediction model for readmission within 30 days (early readmission or early readmission) of patients based on their profile at index hospitalization for esophagectomy. METHODS: Using the National Readmission Database, 383 patients requiring early readmission out of a total of 2037 esophagectomy patients alive at discharge in 2016 were identified. Early readmission risk factors were identified using standard statistics and after the application of ML methodology, the models were interpreted. RESULTS: Early readmission after esophagectomy connoted an increased severity score and risk of mortality. Chronic obstructive pulmonary disease and malnutrition as well as postoperative prolonged intubation, pneumonia, acute kidney failure, and length of stay were identified as factors most contributing to increased odds of early readmission. The reasons for early readmission were more likely to be cardiopulmonary complications, anastomotic leak, and sepsis/infection. Patients with upper esophageal neoplasms had significantly higher early readmission and patients who received pyloroplasty/pyloromyotomy had significantly lower early readmission. Two ML models to predict early readmission were generated: 1 with 71.7% sensitivity for clinical decision making and the other with 84.8% accuracy and 98.7% specificity for quality review. CONCLUSIONS: We identified risk factors for early readmission after esophagectomy and introduced ML-based techniques to predict early readmission in 2 different settings: clinical decision making and quality review. ML techniques can be utilized to provide targeted support and standardize quality measures.

Predicting respiratory failure after pulmonary lobectomy using machine learning techniques.

BACKGROUND: When pulmonary complications occur, postlobectomy patients have a higher mortality rate, increased length of stay, and higher readmission rates. Because of a lack of high-quality consolidated clinical data, it is challenging to assess and recognize at-risk thoracic patients to avoid respiratory failure and standardize outcome measures. METHODS: The National (Nationwide) Inpatient Sample for 2015 was used to establish our model. We identified 417 respiratory failure from a total of 4,062 patients who underwent pulmonary lobectomy. Risk factors for respiratory failure were identified, analyzed, and used in novel machine learning models to predict respiratory failure. RESULTS: Factors that contributed to increased odds of respiratory failure, such as preexisting chronic diseases, and intraoperative and postoperative events during hospitalization were identified. Two machine learning-based prediction models were generated and optimized by the knowledge accrued from the clinical course of postlobectomy patients. The first model, with high accuracy and specificity, is suited for performance evaluation, and the second model, with high sensitivity, is suited for clinical decision making. CONCLUSION: We identified risk factors for respiratory failure after lobectomy and introduced 2 machine learning-based techniques to predict respiratory failure for quality review and clinical decision-making settings. Such techniques can be used to not only provide targeted support but also standardize quality peer review measures.

Risk Factors for Early Readmission After Acute Pancreatitis: Importance of Timely Interventions.

BACKGROUND: Despite improvements in the diagnosis and care of acute pancreatitis, the mortality, morbidity, and long-term complications of this disease currently account for an annual cost of $10 billion in the United States. Lack of high-quality consolidated clinical data about this ever-increasing national and global burden makes it challenging to be able to recognize at-risk populations and intervene to avoid early readmission (ER) (i.e., readmission within 30 d of hospital discharge or ER). METHODS: We reviewed the National Readmission Database for 2016. We retrieved 25,476 ER out of a total of 188,757 patients admitted with acute pancreatitis (ICD-10 diagnosis of K85), alive at discharge. Patients younger than 18 at the time of initial admission were excluded. Diagnostic characteristics and procedures performed were extracted from ICD-10 data. Based on patient demographics and the diagnostic and procedural profiles from their initial admission, we identified clusters of risk factors for ER using agglomerative hierarchical clustering. These are depicted in a correlation matrix. RESULTS: Acute pancreatitis is associated with a 13.5% overall ER rate. Certain pre-existing chronic diseases, particularly cardiovascular disease diagnoses and interventions at initial presentation increase the odds of ER. In contrast to interventions on the pancreas, interventions on the biliary system correlated with lower odds of ER. Furthermore, the earlier the biliary system intervention was performed during the initial hospitalization, the lower the odds of ER. We identified five clusters of interrelationships: age/comorbidity cluster, cirrhosis cluster, sepsis/pulmonary complication cluster, biliary intervention cluster, and high-risk of mortality cluster. CONCLUSIONS: We identified several potentially modifiable risk factors for ER of patients hospitalized with acute pancreatitis, which included timing of biliary interventions. Furthermore, we identified clusters of interrelationships that further illuminate which complications tend to occur concomitantly and ultimately contribute to ER. By identifying risk factors and elucidating their interactions, we have improved our understanding of this highly morbid disease and offer potential points of intervention to reduce ER.

Peripherally inserted central venous catheter safety in burn care: a single-center retrospective cohort review.

The use of peripherally inserted central catheter (PICC) line for central venous access in thermally injured patients has increased in recent years despite a lack of evidence regarding safety in this patient population. A recent survey of invasive catheter practices among 44 burn centers in the United States found that 37% of burn units use PICC lines as part of their treatment protocol. The goal of this study was to compare PICC-associated complication rates with the existing literature in both the critical care and burn settings. The methodology involved is a single institution retrospective cohort review of patients who received a PICC line during admission to a regional burn unit between 2008 and 2013. Fifty-three patients were identified with a total of seventy-three PICC lines. The primary outcome measurement for this study was indication for PICC line discontinuation. The most common reason for PICC line discontinuation was that the line was no longer indicated (45.2%). Four cases of symptomatic upper extremity deep vein thrombosis (5.5%) and three cases of central line-associated bloodstream infection (4.3%, 2.72 infections per 1000 line days) were identified. PICC lines were in situ an average of 15 days (range 1 to 49 days). We suggest that PICC line-associated complication rates are similar to those published in the critical care literature. Though these rates are higher than those published in the burn literature, they are similar to central venous catheter-associated complication rates. While PICC lines can be a useful resource in the treatment of the thermally injured patient, they are associated with significant and potentially fatal risks.