Vaccination impact: mortality and time shift to COVID-19 maximum severity in hospitalized patients. An Argentine multicenter registry.

INTRODUCTION: The COVID-19 vaccine became an effective instrument to prevent severe SARS-CoV-2 infections. However, 5% of vaccinated patients will have moderate or severe disease. OBJECTIVE: to compare mortality and days between the symptom onset to the peak disease severity, in vaccinated vs. unvaccinated COVID-19 hospitalized patients. METHODS: Retrospective observational study in 36 hospitals in Argentina. COVID-19 adults admitted to general wards between January 1, 2021, and May 31, 2022 were included. Days between symptoms onset to peak of severity were compared between vaccinated vs. unvaccinated patients with Cox regression, adjusted by Propensity Score Matching (PSM). Results in patients with one and two doses were also compared. RESULTS: A total of 3663 patients were included (3001 [81.9%] unvaccinated and 662 [18%] vaccinated). Time from symptom onset to peak severity was 7 days (IQR 4-12) vs. 7 days (IQR 4-11) in unvaccinated and vaccinated. In crude Cox regression analysis and matched population, no significant differences were observed. Regarding mortality, a Risk Ratio (RR) of 1.51 (IC95% 1.29-1.77) was observed in vaccinated patients, but in the PSM cohort, the RR was 0.73 (IC95% 0.60-0.88). RR in patients with one COVID-19 vaccine dose in PSM adjusted population was 0.7 (IC95% 0.45-1.03), and with two doses 0.6 (IC95% 0.46-0.79). DISCUSSION: The time elapsed between the onset of COVID-19 symptoms to the highest severity was similar in vaccinated and unvaccinated patients. However, hospitalized vaccinated patients had a lower risk of mortality than unvaccinated patients.

Introducción: A pesar de la eficacia de la vacuna contra el COVID-19 el 5% de los pacientes vacunados presentaran una enfermedad moderada o grave. El objetivo del presente estudio fue comparar los días entre el inicio de los síntomas y la gravedad máxima de la enfermedad, en pacientes con COVID-19 vacunados vs. no vacunados. Métodos: Estudio observacional retrospectivo en 36 hospitales de Argentina. Se incluyeron adultos con COVID-19 hospitalizados entre el 1/01/2021 y 31/5/2022. Se recolectaron datos demográficos, comorbilidades y progresión clínica de la enfermedad. Se compararon los días entre el inicio de los síntomas y el pico de gravedad entre vacunados y no vacunados mediante regresión de Cox, ajustada por emparejamiento por Propensity Score Matching (PSM). En un análisis de subgrupos, se compararon los resultados en pacientes con una y dos dosis de vacuna. Resultados: Se incluyeron 3663 pacientes (3001 [81.9%] no vacunados y 662 [18%] vacunados). El tiempo transcurrido desde el inicio de los síntomas hasta el pico de gravedad fue de 7 días (IQR 4 - 12) en no vacunados, y de 7 días (IQR 4-11) en vacunados. Tanto en el análisis de regresión de Cox crudo como en el ajustado, no se observaron diferencias significativas entre ambos grupos (HR ajustado 1.08 [IC 95% 0.82-1.4; p = 0.56]). En cuanto a la mortalidad, el Riesgo Relativo (RR) fue 1.51 (IC95% 1.29-1.77) en los pacientes vacunados, pero en la cohorte ajustada por Propensity Score, el RR fue de 0.73 (IC95% 0.60-0.88). El RR en el grupo con una dosis de vacuna COVID-19 en el análisis PSM fue 0.7 (IC95% 0.45-1.03), y con dos dosis 0.6 (IC95% 0.46-0.79). Discusión: El tiempo entre el inicio de los síntomas de COVID-19 y el pico de severidad fue igual en vacunados y no vacunados. Sin embargo, los pacientes vacunados hospitalizados presentaron menor mortalidad tras el ajuste por confundidores.

Clinical Research With Large Language Models Generated Writing-Clinical Research with AI-assisted Writing (CRAW) Study.

IMPORTANCE: The scientific community debates Generative Pre-trained Transformer (GPT)-3.5's article quality, authorship merit, originality, and ethical use in scientific writing. OBJECTIVES: Assess GPT-3.5's ability to craft the background section of critical care clinical research questions compared to medical researchers with H-indices of 22 and 13. DESIGN: Observational cross-sectional study. SETTING: Researchers from 20 countries from six continents evaluated the backgrounds. PARTICIPANTS: Researchers with a Scopus index greater than 1 were included. MAIN OUTCOMES AND MEASURES: In this study, we generated a background section of a critical care clinical research question on "acute kidney injury in sepsis" using three different methods: researcher with H-index greater than 20, researcher with H-index greater than 10, and GPT-3.5. The three background sections were presented in a blinded survey to researchers with an H-index range between 1 and 96. First, the researchers evaluated the main components of the background using a 5-point Likert scale. Second, they were asked to identify which background was written by humans only or with large language model-generated tools. RESULTS: A total of 80 researchers completed the survey. The median H-index was 3 (interquartile range, 1-7.25) and most (36%) researchers were from the Critical Care specialty. When compared with researchers with an H-index of 22 and 13, GPT-3.5 was marked high on the Likert scale ranking on main background components (median 4.5 vs. 3.82 vs. 3.6 vs. 4.5, respectively; p < 0.001). The sensitivity and specificity to detect researchers writing versus GPT-3.5 writing were poor, 22.4% and 57.6%, respectively. CONCLUSIONS AND RELEVANCE: GPT-3.5 could create background research content indistinguishable from the writing of a medical researcher. It was marked higher compared with medical researchers with an H-index of 22 and 13 in writing the background section of a critical care clinical research question.

Development and validation of COEWS (COVID-19 Early Warning Score) for hospitalized COVID-19 with laboratory features: A multicontinental retrospective study.

Background: The emergence of new SARS-CoV-2 variants with significant immune-evasiveness, the relaxation of measures for reducing the number of infections, the waning of immune protection (particularly in high-risk population groups), and the low uptake of new vaccine boosters, forecast new waves of hospitalizations and admission to intensive care units. There is an urgent need for easily implementable and clinically effective Early Warning Scores (EWSs) that can predict the risk of complications within the next 24-48 hr. Although EWSs have been used in the evaluation of COVID-19 patients, there are several clinical limitations to their use. Moreover, no models have been tested on geographically distinct populations or population groups with varying levels of immune protection. Methods: We developed and validated COVID-19 Early Warning Score (COEWS), an EWS that is automatically calculated solely from laboratory parameters that are widely available and affordable. We benchmarked COEWS against the widely used NEWS2. We also evaluated the predictive performance of vaccinated and unvaccinated patients. Results: The variables of the COEWS predictive model were selected based on their predictive coefficients and on the wide availability of these laboratory variables. The final model included complete blood count, blood glucose, and oxygen saturation features. To make COEWS more actionable in real clinical situations, we transformed the predictive coefficients of the COEWS model into individual scores for each selected feature. The global score serves as an easy-to-calculate measure indicating the risk of a patient developing the combined outcome of mechanical ventilation or death within the next 48 hr.The discrimination in the external validation cohort was 0.743 (95% confidence interval [CI]: 0.703-0.784) for the COEWS score performed with coefficients and 0.700 (95% CI: 0.654-0.745) for the COEWS performed with scores. The area under the receiver operating characteristic curve (AUROC) was similar in vaccinated and unvaccinated patients. Additionally, we observed that the AUROC of the NEWS2 was 0.677 (95% CI: 0.601-0.752) in vaccinated patients and 0.648 (95% CI: 0.608-0.689) in unvaccinated patients. Conclusions: The COEWS score predicts death or MV within the next 48 hr based on routine and widely available laboratory measurements. The extensive external validation, its high performance, its ease of use, and its positive benchmark in comparison with the widely used NEWS2 position COEWS as a new reference tool for assisting clinical decisions and improving patient care in the upcoming pandemic waves. Funding: University of Vienna.

Risk of Volar Locking Plate Removal After Distal Radius Fractures: Time-to-Event Analysis.

PURPOSE: The primary purpose of this study was to describe the rate of volar locking plate (VLP) removal after distal radius fracture and how long it takes for the risk of VLP removal to stabilize. The secondary purpose was to describe the reasons for VLP removal and analyze the relationship between it and the Soong index. METHODS: This was a single-center retrospective cohort study. Patients aged >18 years with distal radius fracture who underwent VLP fixation were included. Hardware removal, time until VLP removal, and the primary reason for removal were recorded. The implant prominence was measured as described by Soong. We used Kaplan-Meier curves and risk tables to describe the risk of VLP removal and variation over time. Multivariable logistic regression was used to assess the relationship between Soong grade and VLP removal. RESULTS: A total of 313 wrists were included. There were 35 cases of VLP removal, with an overall incidence of 11.2% at 15 years of follow-up. The incidence rate was 1.2 per 100 individuals per year for the entire cohort. The risk of VLP removal decreased from 6.2% in the first postoperative year to 1.7% in the second year and 1.4% in the third year. Beyond that, the rate remained <1% per year throughout the follow-up period. The median hardware removal time was 11 months. The main reasons for VLP removal were tenosynovitis, implant-associated pain, and screw protrusion. We found no association between Soong grade and VLP removal. CONCLUSIONS: Volar locking plate removal after distal radius fracture was more common in the first year after surgery and remained notable until the third year. Regular monitoring and patient education to assess possible complications related to hardware are important during this period. TYPE OF STUDY/LEVEL OF EVIDENCE: Therapeutic IV.

Association of plasma volume status with outcomes in hospitalized Covid-19 ARDS patients: A retrospective multicenter observational study.

PURPOSE: To evaluate the association of estimated plasma volume (ePV) and plasma volume status (PVS) on admission with the outcomes in COVID-19-related acute respiratory distress syndrome (ARDS) patients. MATERIALS AND METHODS: We performed a retrospective multi-center study on COVID-19-related ARDS patients who were admitted to the Mayo Clinic Enterprise health system. Plasma volume was calculated using the formulae for ePV and PVS, and these variables were analyzed for correlation with patient outcomes. RESULTS: Our analysis included 1298 patients with sequential organ failure assessment (SOFA) respiratory score ≥ 2 (PaO2/FIO2 ≤300 mmHg) and a mortality rate of 25.96%. A Cox proportional multivariate analysis showed PVS but not ePV as an independent correlation with 90-day mortality after adjusting for the covariates (HR: 1.015, 95% CI: 1.005-1.025, p = 0.002 and HR 1.054, 95% CI 0.958-1.159, p = 0.278 respectively). CONCLUSION: A lower PVS on admission correlated with a greater chance of survival in COVID-19-related ARDS patients. The role of PVS in guiding fluid management should be investigated in future prospective studies.

COVID-19 vaccines reduce mortality in hospitalized patients with oxygen requirements: Differences between vaccine subtypes. A multicontinental cohort study.

The aim of this study was to analyze whether the coronavirus disease 2019 (COVID-19) vaccine reduces mortality in patients with moderate or severe COVID-19 disease requiring oxygen therapy. A retrospective cohort study, with data from 148 hospitals in both Spain (111 hospitals) and Argentina (37 hospitals), was conducted. We evaluated hospitalized patients for COVID-19 older than 18 years with oxygen requirements. Vaccine protection against death was assessed through a multivariable logistic regression and propensity score matching. We also performed a subgroup analysis according to vaccine type. The adjusted model was used to determine the population attributable risk. Between January 2020 and May 2022, we evaluated 21,479 COVID-19 hospitalized patients with oxygen requirements. Of these, 338 (1.5%) patients received a single dose of the COVID-19 vaccine and 379 (1.8%) were fully vaccinated. In vaccinated patients, mortality was 20.9% (95% confidence interval [CI]: 17.9-24), compared to 19.5% (95% CI: 19-20) in unvaccinated patients, resulting in a crude odds ratio (OR) of 1.07 (95% CI: 0.89-1.29; p = 0.41). However, after considering the multiple comorbidities in the vaccinated group, the adjusted OR was 0.73 (95% CI: 0.56-0.95; p = 0.02) with a population attributable risk reduction of 4.3% (95% CI: 1-5). The higher risk reduction for mortality was with messenger RNA (mRNA) BNT162b2 (Pfizer) (OR 0.37; 95% CI: 0.23-0.59; p < 0.01), ChAdOx1 nCoV-19 (AstraZeneca) (OR 0.42; 95% CI: 0.20-0.86; p = 0.02), and mRNA-1273 (Moderna) (OR 0.68; 95% CI: 0.41-1.12; p = 0.13), and lower with Gam-COVID-Vac (Sputnik) (OR 0.93; 95% CI: 0.6-1.45; p = 0.76). COVID-19 vaccines significantly reduce the probability of death in patients suffering from a moderate or severe disease (oxygen therapy).

Alactic base excess is an independent predictor of death in sepsis: A propensity score analysis.

PURPOSE: Alactic base excess (ABE) is a novel biomarker defined as the sum of lactate and standard base excess and estimates the renal capability of handling acid-base disturbances in sepsis. The objective of this study is to see if ABE is an independent predictor of mortality in septic patients with and without renal dysfunction. MATERIALS AND METHODS: We retrospectively studied 1178 patients with sepsis and septic shock. Patients were divided according to ABE values: 1) negative ABE (<-3 mmol/L); 2) neutral ABE (≥ - 3 and < 4 mmol/L); and 3) positive ABE (≥4 mmol/L). The effect of ABE on mortality was evaluated using Cox regression weight by inverse probability weighting (IPWT) analysis after propensity score assessment. Additionally, we performed a stratified analysis in patients with GFR > 60 mL/min/1.73 m2. RESULTS: Negative ABE patients had higher mortality than patients with neutral ABE (adjusted HR 1.43; 95%CI 1.02-2.01). Also, in patients with GFR > 60 mL/min/1.73 m2 (n = 493), we observed higher mortality in patients with negative ABE (adjusted HR 2.43; 95%CI 1.07-5.53). CONCLUSIONS: Negative ABE is an independent predictor of in-hospital mortality in septic patients with and without renal dysfunction.

Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study.

New SARS-CoV-2 variants, breakthrough infections, waning immunity, and sub-optimal vaccination rates account for surges of hospitalizations and deaths. There is an urgent need for clinically valuable and generalizable triage tools assisting the allocation of hospital resources, particularly in resource-limited countries. We developed and validate CODOP, a machine learning-based tool for predicting the clinical outcome of hospitalized COVID-19 patients. CODOP was trained, tested and validated with six cohorts encompassing 29223 COVID-19 patients from more than 150 hospitals in Spain, the USA and Latin America during 2020-22. CODOP uses 12 clinical parameters commonly measured at hospital admission for reaching high discriminative ability up to 9 days before clinical resolution (AUROC: 0·90-0·96), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. Furthermore, CODOP maintains its predictive ability independently of the virus variant and the vaccination status. To reckon with the fluctuating pressure levels in hospitals during the pandemic, we offer two online CODOP calculators, suited for undertriage or overtriage scenarios, validated with a cohort of patients from 42 hospitals in three Latin American countries (78-100% sensitivity and 89-97% specificity). The performance of CODOP in heterogeneous and geographically disperse patient cohorts and the easiness of use strongly suggest its clinical utility, particularly in resource-limited countries.

While COVID-19 vaccines have saved millions of lives, new variants, waxing immunity, unequal rollout and relaxation of mitigation strategies mean that the pandemic will keep on sending shockwaves across healthcare systems. In this context, it is crucial to equip clinicians with tools to triage COVID-19 patients and forecast who will experience the worst forms of the disease. Prediction models based on artificial intelligence could help in this effort, but the task is not straightforward. Indeed, the pandemic is defined by ever-changing factors which artificial intelligence needs to cope with. To be useful in the clinic, a prediction model should make accurate prediction regardless of hospital location, viral variants or vaccination and immunity statuses. It should also be able to adapt its output to the level of resources available in a hospital at any given time. Finally, these tools need to seamlessly integrate into clinical workflows to not burden clinicians. In response, Klén et al. built CODOP, a freely available prediction algorithm that calculates the death risk of patients hospitalized with COVID-19 (https://gomezvarelalab.em.mpg.de/codop/). This model was designed based on biochemical data from routine blood analyses of COVID-19 patients. Crucially, the dataset included 30,000 individuals from 150 hospitals in Spain, the United States, Honduras, Bolivia and Argentina, sampled between March 2020 and February 2022 and carrying most of the main COVID-19 variants (from the original Wuhan version to Omicron). CODOP can predict the death or survival of hospitalized patients with high accuracy up to nine days before the clinical outcome occurs. These forecasting abilities are preserved independently of vaccination status or viral variant. The next step is to tailor the model to the current pandemic situation, which features increasing numbers of infected people as well as accumulating immune protection in the overall population. Further development will refine CODOP so that the algorithm can detect who will need hospitalisation in the next 24 hours, and who will need admission in intensive care in the next two days. Equipping primary care settings and hospitals with these tools will help to restore previous standards of health care during the upcoming waves of infections, particularly in countries with limited resources.