Severity Of Outcomes In Patients Research Articles

Changes and Rate of Change in Neutrophil-Lymphocyte Ratio (∆NLR) as an Early Prognostic Marker for the Severity of Outcomes in Patients With COVID-19 and Its Applicability in Other Viral and Bacterial Diseases.

COVID-19 is a global pandemic that has spread rapidly and resulted in numerous deaths worldwide. Many inflammatory markers such as neutrophil-lymphocyte ratio (NLR), D-dimer, serum ferritin, C-reactive protein (CRP), and interleukin-6 (IL-6) were used for the diagnosis and prognosis of COVID-19. We have proposed using Delta NLR (0-48 hours) (∆NLR) as an early diagnostic marker for COVID-19 and other inflammatory disorders. We have created a prediction model based on six variables: overall severity, death, shifting to the ICU, length of stay, oxygen requirement, and ventilator support. Prediction models help us prepare for future pandemics through early diagnosis and management. A total of 1,865 patient records were retrieved from the database. The final sample available for analysis was 461. Change in NLR or ∆NLR was significant for all the models (except for length of stay) created by logistic regression. An independent predictor of the poor prognosis of COVID-19 is the severity of the disease in the initial one or two days. ∆NLR is a unique marker, and its scope of use in other disorders' prognoses must be further researched. The prediction models also help us in decision-making strategies and also prepare us for future pandemics.

Elevated Neutrophil Gelatinase-Associated Lipocalin Is Associated With the Severity of Kidney Injury and Poor Prognosis of Patients With COVID-19

IntroductionLoss of kidney function is a common feature of COVID-19 infection, but serum creatinine (SCr) is not a sensitive or specific marker of kidney injury. We tested whether molecular biomarkers of tubular injury measured at hospital admission were associated with acute kidney injury (AKI) in those with COVID-19 infection.MethodsThis is a prospective cohort observational study consisting of 444 consecutive patients with SARS-CoV-2 enrolled in the Columbia University emergency department (ED) at the peak of the pandemic in New York (March 2020–April 2020). Urine and blood were collected simultaneously at hospital admission (median time: day 0, interquartile range: 0–2 days), and urine biomarkers were analyzed by enzyme-linked immunosorbent assay (ELISA) and a novel dipstick. Kidney biopsies were probed for biomarker RNA and for histopathologic acute tubular injury (ATI) scores.ResultsAdmission urinary neutrophil gelatinase-associated lipocalin (uNGAL) level was associated with AKI diagnosis (267 ± 301 vs. 96 ± 139 ng/ml, P < 0.0001) and staging; uNGAL levels >150 ng/ml had 80% specificity and 75% sensitivity to diagnose AKI stages 2 to 3. Admission uNGAL level quantitatively associated with prolonged AKI, dialysis, shock, prolonged hospitalization, and in-hospital death, even when admission SCr level was not elevated. The risk of dialysis increased almost 4-fold per SD of uNGAL independently of baseline SCr, comorbidities, and proteinuria (odds ratio [OR] [95% CI]: 3.59 [1.83–7.45], P < 0.001). In the kidneys of those with COVID-19, NGAL mRNA expression broadened in parallel with severe histopathologic injury (ATI). Conversely, low uNGAL levels at admission ruled out stages 2 to 3 AKI (negative predictive value: 0.95, 95% CI: 0.92–0.97) and the need for dialysis (negative predictive value: 0.98, 95% CI: 0.96–0.99). Although proteinuria and urinary (u)KIM-1 were implicated in tubular injury, neither was diagnostic of AKI stages.ConclusionIn the patients with COVID-19, uNGAL level was quantitatively associated with histopathologic injury (ATI), loss of kidney function (AKI), and severity of patient outcomes.

Abstract 234: Risk of sepsis among patients with prostate cancer: A network-based modeling approach

Abstract Background: Prostate cancer (PCa) is the most common cancer among men, and sepsis is a leading cause of death in PCa, yet their association has not been quantified. This study aims to quantify the association between sepsis severity (identified as sepsis, severe sepsis, and septic shock) and prostate complications among PCa subgroups. Methods: We used the SEER-Medicare data to identify patients diagnosed with PCa in 2010 -2015. PCa patients were classified as: localized or regional (L/R) stage and distant (D) stage and further stratified as survivors and non-survivors. Patients with no history of PCa composed the control group. Our network-based modeling approach used sepsis and prostate complications (identified using ICD codes) as network nodes. The association between complication pairs, represented as edges, were weighted by the probability of complication pairs occurring concurrently. The network densities for each subgroup were computed at different thresholds based on the weights of the edges. Results: We identified 27,166 patients with PCa and 22,223 as controls. Increase in threshold was associated with highest decline in network density among controls followed by L/R survivors (Table). Additionally, prostate and sepsis complications co-occur more frequently among stage D than stage L/R patients. Also, stage D patients experience prostate complications with more severe sepsis conditions (severe sepsis and septic shock) than other subpopulations. The variances and medians of edges' weights among all subgroups were statistically different (p&amp;lt;0.001). Conclusions: This is the first population-based study using network-based modeling to quantify the association between varying degrees of sepsis severity and prostate complications among PCa subgroups. The differentiating complication pairs identified among subgroups can be used as features in machine-learning algorithms to predict the severity of patient outcomes and to improve patient prognosis. Table: Network densities of sepsis and PCa complication pairs at different weight thresholds for each PCa subgroupsNetwork densities at various threshold weightsQuantiles of Weights in Network (Median (Q1,Q3))Threshold Levels0.0000.0100.0200.0300.040Control0.1930.0050.0030.0010.0010.005 (0.002, 0.016)Stage L\R survivors0.2410.0290.0110.0070.0050.007 (0.003, 0.021)Stage L\R non-survivors0.1580.0380.0140.0070.0040.012 (0.005, 0.029)Stage D survivors0.0840.0270.0060.0020.0010.013 (0.006, 0.032)Stage D non-survivors0.1300.0360.0170.0090.0040.012 (0.005, 0.029) Citation Format: Ali Jazayeri, Niusha Jafari, Christopher C. Yang, Nikita Nikita, Grace Lu Yao. Risk of sepsis among patients with prostate cancer: A network-based modeling approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 234.

Anesthesia Adverse Events Voluntarily Reported in the Veterans Health Administration and Lessons Learned.

Anesthesia providers have long been pioneers in patient safety. Despite remarkable efforts, anesthesia errors still occur, resulting in complications, injuries, and even death. The Veterans Health Administration (VHA) National Center of Patient Safety uses root cause analysis (RCA) to examine why system-related adverse events occur and how to prevent future similar events. This study describes the types of anesthesia adverse events reported in VHA hospitals and their root causes and preventative actions. RCA reports from VHA hospitals from May 30, 2012, to May 1, 2015, were reviewed for root causes, severity of patient outcomes, and actions. These elements were coded by consensus and analyzed using descriptive statistics. During the study period, 3228 RCAs were submitted, of which 292 involved an anesthesia provider. Thirty-six of these were specific to anesthesia care. We reviewed these 36 RCA reports of adverse events specific to anesthesia care. Types of event included medication errors (28%, 10), regional blocks (14%, 5), airway management (14%, 5), skin integrity or position (11%, 4), other (11%, 4), consent issues (8%, 3), equipment (8%, 3), and intravenous access and anesthesia awareness (3%, 1 each). Of the 36 anesthesia events reported, 5 (14%) were identified as being catastrophic, 10 (28%) major, 12 (34%) moderate, and 9 (26%) minor. The majority of root causes identified a need for improved standardization of processes. This analysis points to the need for systemwide implementation of human factors engineering-based approaches to work toward further eliminating anesthesia-related adverse events. Such actions include standardization of processes, forcing functions, separating storage of look-alike sound-alike medications, limiting stock of high-risk medication strengths, bar coding medications, use of cognitive aids such as checklists, and high-fidelity simulation.

Comparison of outcome of self-extubation and accidental extubation in ICU

Introduction: The study aimed to assess and compare the vulnerability and severity of outcomes in patients who suffered self-extubation and accidental extubation during their stay in the ICU. Design: Prospective observational study. Setting: Sixteen-bedded mixed intensive care unit in a tertiary care hospital. Materials and Methods: All adult patients admitted in ICU with either an endotracheal tube or a tracheostomy were included in the study. The time and description of the type of unplanned extubation, the cause and severity of the incident and its impact on the course of the patient's illness, the person who noted the incident first and how it was detected were noted. Results: The rate of unplanned extubation was 32 (1.42/100 tube days) in 552 intubated patients (2243 tube days). Of them, 26 patients suffered self-extubation while the rest six patients were accidentally extubated. Re-intubation was required in eight patients after self-extubation while it was needed in all the six patients of accidental extubation. Three patients of accidental extubation went on to develop respiratory arrest including one patient who developed cardiac arrest. Conclusion: The outcome of the patients who suffered self-extubation is better than those with accidental extubations.

Galactosemia: The good, the bad, and the unknown

Alpha-D-galactose is metabolized in species ranging from E. coli to mammals predominantly via a series of sequential reactions collectively known as the Leloir pathway. Deficiency of any one of these enzymes in humans results in a form of the inherited metabolic disorder, galactosemia, although the symptoms and severity depend upon the enzyme impaired, and the degree of functional deficiency (Tyfield and Walter, 2002, The Metabolic and Molecular Bases of Inherited Disease. New York: McGraw Hill.). Studies of these enzymes, and the disorders associated with their loss, have led to a much deeper appreciation of the intricate and interwoven levels of regulation that govern their normal function. These insights have further identified likely mediators of outcome severity in patients, and have enabled a rational approach to the development of novel strategies of intervention.