The utility of MEWS for predicting the mortality in the elderly adults with COVID-19: a retrospective cohort study with comparison to other predictive clinical scores

Significance of nitrosative stress and glycoxidation products in the diagnosis of COVID-19

Nitrosative stress promotes protein glycoxidation, and both processes can occur during an infection with the SARS-CoV-2 virus. Therefore, the aim of this study was to assess selected nitrosative stress parameters and protein glycoxidation products in COVID-19 patients and convalescents relative to healthy subjects, including in reference to the severity of COVID-19 symptoms. The diagnostic utility of nitrosative stress and protein glycoxidation biomarkers was also evaluated in COVID-19 patients. The study involved 218 patients with COVID-19, 69 convalescents, and 48 healthy subjects. Nitrosative stress parameters (NO, S-nitrosothiols, nitrotyrosine) and protein glycoxidation products (tryptophan, kynurenine, N-formylkynurenine, dityrosine, AGEs) were measured in the blood plasma or serum with the use of colorimetric/fluorometric methods. The levels of NO (p = 0.0480), S-nitrosothiols (p = 0.0004), nitrotyrosine (p = 0.0175), kynurenine (p < 0.0001), N-formylkynurenine (p < 0.0001), dityrosine (p < 0.0001), and AGEs (p < 0.0001) were significantly higher, whereas tryptophan fluorescence was significantly (p < 0.0001) lower in COVID-19 patients than in the control group. Significant differences in the analyzed parameters were observed in different stages of COVID-19. In turn, the concentrations of kynurenine (p < 0.0001), N-formylkynurenine (p < 0.0001), dityrosine (p < 0.0001), and AGEs (p < 0.0001) were significantly higher, whereas tryptophan levels were significantly (p < 0.0001) lower in convalescents than in healthy controls. The ROC analysis revealed that protein glycoxidation products can be useful for diagnosing infections with the SARS-CoV-2 virus because they differentiate COVID-19 patients (KN: sensitivity-91.20%, specificity-92.00%; NFK: sensitivity-92.37%, specificity-92.00%; AGEs: sensitivity-99,02%, specificity-100%) and convalescents (KN: sensitivity-82.22%, specificity-84.00%; NFK: sensitivity-82,86%, specificity-86,00%; DT: sensitivity-100%, specificity-100%; AGE: sensitivity-100%, specificity-100%) from healthy subjects with high sensitivity and specificity. Nitrosative stress and protein glycoxidation are intensified both during and after an infection with the SARS-CoV-2 virus. The levels of redox biomarkers fluctuate in different stages of the disease. Circulating biomarkers of nitrosative stress/protein glycoxidation have potential diagnostic utility in both COVID-19 patients and convalescents.

Effectiveness of Implementing Modified Early Warning System and Rapid Response Team for General Ward Inpatients

This retrospective study assessed the effectiveness and impact of implementing a Modified Early Warning System (MEWS) and Rapid Response Team (RRT) for inpatients admitted to the general ward (GW) of a medical center. This study included all inpatients who stayed in GWs from Jan. 2017 to Feb. 2022. We divided inpatients into GWnon-MEWS and GWMEWS groups according to MEWS and RRT implementation in Aug. 2019. The primary outcome, unexpected deterioration, was defined by unplanned admission to intensive care units. We defined the detection performance and effectiveness of MEWS according to if a warning occurred within 24 h before the unplanned ICU admission. There were 129,039 inpatients included in this study, comprising 58,106 GWnon-MEWS and 71,023 GWMEWS. The numbers of inpatients who underwent an unplanned ICU admission in GWnon-MEWS and GWMEWS were 488 (.84%) and 468 (.66%), respectively, indicating that the implementation significantly reduced unexpected deterioration (p < .0001). Besides, 1,551,525 times MEWS assessments were executed for the GWMEWS. The sensitivity, specificity, positive predicted value, and negative predicted value of the MEWS were 29.9%, 98.7%, 7.09%, and 99.76%, respectively. A total of 1,568 warning signs accurately occurred within the 24 h before an unplanned ICU admission. Among them, 428 (27.3%) met the criteria for automatically calling RRT, and 1,140 signs necessitated the nursing staff to decide if they needed to call RRT. Implementing MEWS and RRT increases nursing staff's monitoring and interventions and reduces unplanned ICU admissions.

Vaccines reduced hospital length of stay and fraction of inspired oxygen of COVID-19 patients: A retrospective cohort study

Few studies have focused on the evaluation of vaccine effectiveness (VE) in mainland China. This study was to characterize the VE including the frequent symptoms, laboratory indices, along with endotracheal intubation, hospital length of stay (LoS), and survival status. This retrospective cohort study included patients with COVID-19 admitted to our hospital. Statistical comparisons of continuous variables were carried out with an independent Student's t-test or Mann-Whitney U test. For categorical variables, the Chi-square test and Fisher exact test were used. Multivariable regression analysis was performed to adjust the confounding factors such as age, gender, body mass index (BMI), residential area, smoking status, the Charlson comorbidity index (CCI) score, followed by investigating the effects of vaccination on critical ill prevention, reduced mortality and endotracheal intubation, LoS and inspired oxygen. This study included 549 hospitalized patients with COVID-19, including 222 (40.43 %) vaccinated participants and 327 (59.57 %) unvaccinated counterparts. There was no obvious difference between the two groups in typical clinical symptoms of COVID-19, clinical laboratory results and mortality. Multivariable analysis showed that COVID-19 vaccine obviously reduced LoS by 1.2 days (lnLoS = -0.14, 95 %CI[-0.24,-0.04]; P = 0.005) and decreased fraction of inspired oxygen by 40 % (OR: 0.60; 95 %CI[0.40,0.90]; P = 0.013) after adjusting age, gender, BMI, residential area, smoking status and CCI score. In contrast, vaccination induced reduction in the critically ill, mortality, and endotracheal intubation compared with the unvaccinated counterparts, but with no statistical differences. Vaccinated patients hospitalized with COVID-19 have a reduced LoS and fraction of inspired oxygen compared to unvaccinated cases in China.

Changes in chemokine and growth factor levels may be useful biomarkers for monitoring disease severity in COVID-19 patients; a pilot study

Aim

The aim of the present study was to assess differences in the serum levels of chemokines and growth factors (GFs) between COVID-19 patients and healthy controls. The diagnostic utility of the analyzed proteins for monitoring the severity of the SARS-CoV- 2 infection based on the patients' MEWS scores was also assessed.

Materials and methods

The serum levels of chemokines and growth factors were analyzed in hospitalized COVID-19 patients (50 women, 50 men) with the use of the Bio-Plex Pro™ Human Cytokine Screening Panel (Biorad) and the Bio-Plex Multiplex system.

Results

The study demonstrated that serum levels of MIP-1α, RANTES, Eotaxin, CTACK, GRO-α, IP-10, MIG, basic-FGF, HGF, SCGF-β, G-CSF, M-CSF, SCF, MIF, LIF, and TRAIL were significant higher in COVID-19 patients than in the control group. The concentrations of CTACK, GRO-α, IP-10, MIG, basic-FGF, HGF, PDGF- BB, GM-CSF, SCF, LIF, and TRAIL were higher in asymptomatic/mildly symptomatic COVID-19 patients (stage 1) and COVID-19 patients with pneumonia without respiratory failure (stage 2). The receiver operating characteristic (ROC) analysis revealed that IP-10, MIF, MIG, and basic-FGF differentiated patients with COVID-19 from healthy controls with the highest sensitivity and specificity, whereas GM-CSF, basic-FGF, and MIG differentiated asymptomatic/mildly symptomatic COVID-19 patients (stage 1) from COVID-19 patients with pneumonia without respiratory failure (stage 2) with the highest sensitivity and specificity.

Conclusions

MIG, basic-FGF, and GM-CSF can be useful biomarkers for monitoring disease severity in patients with COVID-19.

Increased carotid intima-media thickness is associated with higher odds of unfavorable outcomes in adults without advanced vascular diseases presenting with non-severe COVID-19 pneumonia: a nested case-control study

AIM

To evaluate the association between carotid intima-media thickness (CIMT) at hospital admission and unfavorable outcomes in adults without advanced vascular diseases presenting with non-severe COVID-19 pneumonia to assess the feasibility of evaluating CIMT as a risk stratification aid in this setting.

METHODS

This proof-of-concept nested case-control study enrolled consecutive non-vaccinated adults free of advanced vascular diseases presenting with verified non-severe COVID-19 pneumonia between December 2020 and June 2021. CIMT was measured at admission, and patients were managed in line with the national Ministry of Health guidelines. Those who died or required mechanical ventilation (MV) during the index hospital stay were considered cases and were matched (entropy balancing, exact matching) on a set of covariates to survivors not requiring MV (controls). Frequentist and Bayesian logistic models were fitted to the case status.

RESULTS

The study enrolled 207 patients: 27 (13%) cases and 180 controls. All were retained in the analysis after entropy balancing, while 27 cases were exactly matched to 99 controls. Higher CIMT at the proximal internal carotid artery (both left and right) was consistently associated with higher odds of being a case: all odds ratio point-estimates were ≥1.50 with lower limits of the 99% confidence intervals/credibility intervals ≥1.00 with two-sided probabilities of OR>1.00 greater than 99.5%. The susceptibility of the estimates to unmeasured confounding was low.

CONCLUSION

This study supports the feasibility of CIMT as a risk stratification aid in adults free of advanced vascular disease presenting with non-severe COVID-19 pneumonia.

Modified Early Warning Score: Clinical Deterioration of Mexican Patients Hospitalized with COVID-19 and Chronic Disease

The objective was to evaluate the Modified Early Warning Score in patients hospitalized for COVID-19 plus chronic disease.

METHODS

Retrospective observational study, 430 hospitalized patients with COVID-19 and chronic disease. Instrument, Modified Early Warning Score (MEWS). Data analysis, with Cox and logistic regression, to predict survival and risk.

RESULTS

Of 430 patients, 58.6% survived, and 41.4% did not. The risk was: low 53.5%, medium 23.7%, and high 22.8%. The MEWS score was similar between survivors 3.02, p 0.373 (95% CI: -0.225-0.597) and non-survivors 3.20 (95% CI: -0.224-0.597). There is a linear relationship between MEWS and mortality risk R 0.920, ANOVA 0.000, constant 4.713, and coefficient 4.406. The Cox Regression p 0.011, with a risk of deterioration of 0.325, with a positive coefficient, the higher the risk, the higher the mortality, while the invasive mechanical ventilation coefficient was negative -0.757. By providing oxygen and ventilation, mortality is lower.

CONCLUSIONS

The predictive value of the modified early warning score in patients hospitalized for COVID-19 and chronic disease is not predictive with the MEWS scale. Additional assessment is required to prevent complications, especially when patients are assessed as low-risk.

Prognostic models in COVID-19 infection that predict severity: a systematic review

Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.

Performance of Six Clinical Physiological Scoring Systems in Predicting In-Hospital Mortality in Elderly and Very Elderly Patients with Acute Upper Gastrointestinal Bleeding in Emergency Department

Background and Objectives: The aim of this study is to compare the performance of six clinical physiological-based scores, including the pre-endoscopy Rockall score, shock index (SI), age shock index (age SI), Rapid Acute Physiology Score (RAPS), Rapid Emergency Medicine Score (REMS), and Modified Early Warning Score (MEWS), in predicting in-hospital mortality in elderly and very elderly patients in the emergency department (ED) with acute upper gastrointestinal bleeding (AUGIB). Materials and Methods: Patients older than 65 years who visited the ED with a clinical diagnosis of AUGIB were enrolled prospectively from July 2016 to July 2021. The six scores were calculated and compared with in-hospital mortality. Results: A total of 336 patients were recruited, of whom 40 died. There is a significant difference between the patients in the mortality group and survival group in terms of the six scoring systems. MEWS had the highest area under the curve (AUC) value (0.82). A subgroup analysis was performed for a total of 180 very elderly patients (i.e., older than 75 years), of whom 27 died. MEWS also had the best predictive performance in this subgroup (AUC, 0.82). Conclusions: This simple, rapid, and obtainable-by-the-bed parameter could assist emergency physicians in risk stratification and decision making for this vulnerable group.

Evaluation of Early Warning Scores on In-Hospital Mortality in COVID-19 Patients: A Tertiary Hospital Study from Taiwan

Coronavirus disease 2019 (COVID-19) remains a global pandemic. Early warning scores (EWS) are used to identify potential clinical deterioration, and this study evaluated the ability of the Rapid Emergency Medicine score (REMS), National Early Warning Score (NEWS), and Modified EWS (MEWS) to predict in-hospital mortality in COVID-19 patients. This study retrospectively analyzed data from COVID-19 patients who presented to the emergency department and were hospitalized between 1 May and 31 July 2021. The area under curve (AUC) was calculated to compare predictive performance of the three EWS. Data from 306 COVID-19 patients (61 ± 15 years, 53% male) were included for analysis. REMS had the highest AUC for in-hospital mortality (AUC: 0.773, 95% CI: 0.69–0.85), followed by NEWS (AUC: 0.730, 95% CI: 0.64–0.82) and MEWS (AUC: 0.695, 95% CI: 0.60–0.79). The optimal cut-off value for REMS was 6.5 (sensitivity: 71.4%; specificity: 76.3%), with positive and negative predictive values of 27.9% and 95.4%, respectively. Computing REMS for COVID-19 patients who present to the emergency department can help identify those at risk of in-hospital mortality and facilitate early intervention, which can lead to better patient outcomes.

Validation of the Acute Physiology and Chronic Health Evaluation (APACHE) II Score in COVID-19 Patients Admitted to the Intensive Care Unit in Times of Resource Scarcity

Introduction During the coronavirus disease 2019 (COVID-19) pandemic, a high number of patients needed to be admitted to the intensive care units (ICUs). Such a high demand led to periods where resources were insufficient and the triage of patients was needed. This study aims to evaluate the performance of the Acute Physiology and Chronic Health Evaluation (APACHE) II as a predictor of mortality in periods where triage protocols were implemented. Methods A single-center, longitudinal, retrospective cohort study was performed on patients admitted to the ICU between January 2020 and December 2021. Patients were divided into two periods: Period 1 (where patients needing ICU admission outnumbered the available resources) and Period 2 (where resources were adequate). The discriminative power of the APACHE II was checked using the receiver operating characteristic (ROC) curves. Calibration was accessed, and survival analysis was performed. Results Data from 428 patients were analyzed (229 in Period 1 and 199 in Period 2). The area under the ROC curve (AUROC) was 0.763 for Period 1 and 0.761 for Period 2, reflecting a good discriminative power. Logistic regression showed the APACHE II to be a significant predictor of mortality. The Hosmer-Lemeshow test demonstrated good calibration. The Youden index was determined, and a log-rank test showed a significantly lower survival for patients with higher APACHE II scores in both periods. Conclusions The APACHE II score is an effective tool in predicting mortality in patients with COVID-19 admitted to the ICU in a period where resource allocation and triage of patients are needed, paving a way for the future development of better and improved triage systems.

The Role of Early Warning Scoring Systems NEWS and MEWS in the Acute Exacerbation of COPD

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are the most devastating events in the course of the disease. Our aim was to investigate the value of early warning scoring systems: National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS) in AECOPD. This is a prospective observational study of patients with AECOPD who were admitted at hospital. The NEWS and MEWS scores were registered at admission (NEWS-d1, MEWS-d1) and on the second day (NEWS-d2, MEWS-d2). A nasopharyngeal and sputum sample was taken for culture. Follow-up was done at 3 and 6 months after hospitalization. Any possible correlations between NEWS and MEWS and other parameters of COPD were explored. A cohort of 64 patients were included. In-hospital mortality was 4.7% while total mortality at 6 months was 26%. We did not find any significant correlation between in-hospital mortality and any of the scores but we could show a higher mortality and more frequent AECOPD at 6 months of follow-up for those with higher NEWS-d2. NEWS-d2 was associated with higher pCO₂ at presentation and a more frequent use of NIV. Higher NEWS-d1 and NEWS-d2 were predictive of a longer hospital stay. The presence of pathogens in the nasopharyngeal sample was related with a higher reduction of both scores on the second day. We therefore support the superiority of NEWS in the evaluation of hospitalized patients with AECOPD. A remaining high NEWS at the second day of hospital stay signals a high risk of hypercapnia and need of NIV but also higher mortality and more frequent exacerbations at 6 months after AECOPD.

Adaptive immune system in severe COVID-19 patients in the first week of illness: A pilot study

Introduction

The presentation of the course of COVID-19-related T-cell responses in the first week of the disease may be a more specific period for adaptive immune response assessment. This study aimed to clarify the relationship between changes in peripheral blood lymphocyte counts and death in patients with COVID-19 pneumonia.

Methods

Thirty-three patients (14 females and 19 males) admitted for severe and desaturated COVID-19 pneumonia confirmed by polymerase chain reaction were included. Lymphocyte subsets and CD4+/CD8+ and CD16+/CD56+ rates were measured using flow cytometry from peripheral blood at admission and on the day of death or hospital discharge.

Results

Twenty-eight patients survived and five died. On the day of admission, the CD4+ cell count was significantly higher and the saturation of O2 was significantly lower in the deceased patients compared to the survivors (P < 0.05). The CD16+/CD56+ rate was significantly lower on the day of death in the deceased patients than in discharge day for the survivors (P = 0.013).

Conclusion

CD4+ lymphocyte percentages and O2 saturation in samples taken on the day of admission to the hospital and CD16+/CD56+ ratios taken at the time of discharge from the hospital were found to be associated with the mortality in patients with severe COVID-19.

Prognostic Value of Physiological Scoring Systems in COVID-19 Patients: A Prospective Observational Study

The objective of this study was to investigate the accuracy of the Modified Early Warning Score (MEWS), Rapid Emergency Medicine Score (REMS), Rapid Acute Physiology Score (RAPS), Worthing Physiological Scoring System (WPSS), and Revised Trauma Score (RTS) for predicting the inhospital mortality of COVID-19 patients. This diagnostic accuracy study was conducted in Tehran, Iran, from November 15, 2020, to March 10, 2021. The participants consisted of 246 confirmed cases of COVID-19 patients who were admitted to the emergency department. The patients were followed from the point of admission up until discharge from the hospital. The mortality status of patients (survivor or nonsurvivor) was reported at the discharge time, and the receiver operating characteristic curve analysis of each scoring system for predicting inhospital mortality was estimated. The area under the curve of REMS was significantly higher than other scoring systems and in cutoff value of 6 and greater had a sensitivity and specificity of 89.13% and 55.50%, respectively. Among the five scoring systems employed in this study, REMS had the best accuracy to predict the inhospital mortality rate of COVID-19 patients and RAPS had the lowest accuracy for inhospital mortality. Thus, REMS is a useful tool that can be employed in identifying high-risk COVID-19 patients.

Ivermectin: A Controversial Focal Point during the COVID-19 Pandemic

The SARS-CoV-2 pandemic has confirmed the apocalyptic predictions that virologists have been making for several decades. The challenge the world is facing is that of trying to find a possible treatment, and a viable and expedient option for addressing this challenge is the repurposing of drugs. However, in some cases, although these drugs are approved for use in humans, the mechanisms of action involved are unknown. In this sense, to justify its therapeutic application to a new disease, it is ideal, but not necessary, to know the basic mechanisms of action involved in a drug's biological effects. This review compiled the available information regarding the various effects attributed to Ivermectin. The controversy over its use for the treatment of COVID-19 is demonstrated by this report that considers the proposal unfeasible because the therapeutic doses proposed to achieve this effect cannot be achieved. However, due to the urgent need to find a treatment, an exhaustive and impartial review is necessary in order to integrate the knowledge that exists, to date, of the possible mechanisms through which the treatment may be helpful in defining safe doses and schedules of Ivermectin.

Prognostic value of National Early Warning Score and Modified Early Warning Score on intensive care unit readmission and mortality: A prospective observational study

Background

Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS) are widely used in predicting the mortality and intensive care unit (ICU) admission of critically ill patients. This study was conducted to evaluate and compare the prognostic value of NEWS and MEWS for predicting ICU readmission, mortality, and related outcomes in critically ill patients at the time of ICU discharge.

Methods

This multicenter, prospective, observational study was conducted over a year, from April 2019 to March 2020, in the general ICUs of two university-affiliated hospitals in Northwest Iran. MEWS and NEWS were compared based on the patients’ outcomes (including mortality, ICU readmission, time to readmission, discharge type, mechanical ventilation (MV), MV duration, and multiple organ failure after readmission) using the univariable and multivariable binary logistic regression. The receiver operating characteristic (ROC) curve was used to determine the outcome predictability of MEWS and NEWS.

Results

A total of 410 ICU patients were enrolled in this study. According to multivariable logistic regression analysis, both MEWS and NEWS were predictors of ICU readmission, time to readmission, MV status after readmission, MV duration, and multiple organ failure after readmission. The area under the ROC curve (AUC) for predicting mortality was 0.91 (95% CI = 0.88–0.94, P < 0.0001) for the NEWS and 0.88 (95% CI = 0.84–0.91, P < 0.0001) for the MEWS. There was no significant difference between the AUC of the NEWS and the MEWS for predicting mortality (P = 0.082). However, for ICU readmission (0.84 vs. 0.71), time to readmission (0.82 vs. 0.67), MV after readmission (0.83 vs. 0.72), MV duration (0.81 vs. 0.67), and multiple organ failure (0.833 vs. 0.710), the AUCs of MEWS were significantly greater (P < 0.001).

Conclusion

National Early Warning Score and MEWS values of >4 demonstrated high sensitivity and specificity in identifying the risk of mortality for the patients’ discharge from ICU. However, we found that the MEWS showed superiority over the NEWS score in predicting other outcomes. Eventually, MEWS could be considered an efficient prediction score for morbidity and mortality of critically ill patients.

COVID‐19 mortality and its predictors in the elderly: A systematic review

Background and Aims

Older people have higher rates of comorbidities and may experience more severe inflammatory responses; therefore, are at higher risk of death. Herein, we aimed to systematically review the mortality in coronavirus disease 2019 (COVID‐19) patients and its predictors in this age group.

Methods

We searched PubMed, Web of Science, and Science Direct using relevant keywords. Retrieved records underwent a two‐step screening process consisting of title/abstract and full‐text screenings to identify the eligible studies.

Results

Summarizing findings of 35 studies demonstrated that older patients have higher mortality rates compared to the younger population. A review of articles revealed that increasing age, body mass index, a male gender, dementia, impairment or dependency in daily activities, presence of consolidations on chest X‐ray, hypoxemic respiratory failure, and lower oxygen saturation at admission were risk factors for death. High d‐dimer levels, 25‐hydroxy vitamin D serum deficiencies, high C‐reactive protein (≥5 mg/L) levels plus any other abnormalities of lymphocyte, higher blood urea nitrogen or lactate dehydrogenase, and higher platelet count were predictors of poor prognosis and mortality in the elderly. Studies have also shown that previous treatment with renin–angiotensin–aldosterone system inhibitors, pharmacological treatments of respiratory disorders, antibiotics, corticosteroids, vitamin K antagonist, antihistamines, azithromycin, Itolizumab (an anti‐CD6 monoclonal antibody) in combination with other antivirals reduces COVID‐19 worsening and mortality. Vaccination against seasonal influenza might also reduce COVID‐19 mortality.

Conclusion

Overall, a critical consideration is necessary for the care and management of COVID‐19 in the aged population considering the drastic contrasts in manifestation and prognosis compared to other age groups. Mortality from COVID‐19 is independently associated with the patient's age. Elderly patients with COVID‐19 are more vulnerable to poor outcomes. Thus, strict preventive measures, timely diagnosis, and aggressive therapeutic/nontherapeutic care are of great importance to reduce acute respiratory distress syndrome and severe complications in older people.

CURB-65, qSOFA, and SIRS Criteria in Predicting In-Hospital Mortality of Critically Ill COVID-19 Patients; a Prognostic Accuracy Study

INTRODUCTION

Outcome prediction of intensive care unit (ICU)-admitted patients is one of the important issues for physicians. This study aimed to compare the accuracy of Quick Sequential Organ Failure Assessment (qSOFA), Confusion, Urea, Respiratory Rate, Blood Pressure and Age Above or Below 65 Years (CURB-65), and Systemic Inflammatory Response Syndrome (SIRS) scores in predicting the in-hospital mortality of COVID-19 patients.

METHODS

This prognostic accuracy study was performed on 225 ICU-admitted patients with a definitive diagnosis of COVID-19 from July to December 2021 in Tehran, Iran. The patients' clinical characteristics were evaluated at the time of ICU admission, and they were followed up until discharge from ICU. The screening performance characteristics of CURB-65, qSOFA, and SIRS in predicting their mortality was compared.

RESULTS

225 patients with the mean age of 63.27±14.89 years were studied (56.89% male). The in-hospital mortality rate of this series of patients was 39.10%. The area under the curve (AUC) of SIRS, CURB-65, and qSOFA were 0.62 (95% CI: 0.55 - 0.69), 0.66 (95% CI: 0.59 - 0.73), and 0.61(95% CI: 0.54 - 0.67), respectively (p = 0.508). In cut-off ≥1, the estimated sensitivity values of SIRS, CURB-65, and qSOFA were 85.23%, 96.59%, and 78.41%, respectively. The estimated specificity of scores were 34.31%, 6.57%, and 38.69%, respectively. In cut-off ≥2, the sensitivity values of SIRS, CURB-65, and qSOFA were evaluated as 39.77%, 87.50%, and 15.91%, respectively. Meanwhile, the specificity of scores were 72.99%, 34.31%, and 92.70%.

CONCLUSIONS

It seems that the performance of SIRS, CURB-65, and qSOFA is similar in predicting the ICU mortality of COVID-19 patients. However, the sensitivity of CURB-65 is higher than qSOFA and SIRS.

COVID-19 mortality and its predictors in the elderly: A systematic review

BACKGROUND AND AIMS

Older people have higher rates of comorbidities and may experience more severe inflammatory responses; therefore, are at higher risk of death. Herein, we aimed to systematically review the mortality in coronavirus disease 2019 (COVID-19) patients and its predictors in this age group.

METHODS

We searched PubMed, Web of Science, and Science Direct using relevant keywords. Retrieved records underwent a two-step screening process consisting of title/abstract and full-text screenings to identify the eligible studies.

RESULTS

Summarizing findings of 35 studies demonstrated that older patients have higher mortality rates compared to the younger population. A review of articles revealed that increasing age, body mass index, a male gender, dementia, impairment or dependency in daily activities, presence of consolidations on chest X-ray, hypoxemic respiratory failure, and lower oxygen saturation at admission were risk factors for death. High d-dimer levels, 25-hydroxy vitamin D serum deficiencies, high C-reactive protein (≥5 mg/L) levels plus any other abnormalities of lymphocyte, higher blood urea nitrogen or lactate dehydrogenase, and higher platelet count were predictors of poor prognosis and mortality in the elderly. Studies have also shown that previous treatment with renin-angiotensin-aldosterone system inhibitors, pharmacological treatments of respiratory disorders, antibiotics, corticosteroids, vitamin K antagonist, antihistamines, azithromycin, Itolizumab (an anti-CD6 monoclonal antibody) in combination with other antivirals reduces COVID-19 worsening and mortality. Vaccination against seasonal influenza might also reduce COVID-19 mortality.

CONCLUSION

Overall, a critical consideration is necessary for the care and management of COVID-19 in the aged population considering the drastic contrasts in manifestation and prognosis compared to other age groups. Mortality from COVID-19 is independently associated with the patient's age. Elderly patients with COVID-19 are more vulnerable to poor outcomes. Thus, strict preventive measures, timely diagnosis, and aggressive therapeutic/nontherapeutic care are of great importance to reduce acute respiratory distress syndrome and severe complications in older people.

Patient Factors and Hospital Outcomes Associated With Atypical Presentation in Hospitalized Older Adults With COVID-19 During the First Surge of the Pandemic

BACKGROUND

Literature indicates an atypical presentation of COVID-19 among older adults (OAs). Our purpose is to identify the frequency of atypical presentation and compare demographic and clinical factors, and short-term outcomes, between typical versus atypical presentations in OAs hospitalized with COVID-19 during the first surge of the pandemic.

METHODS

Data from the inpatient electronic health record were extracted for patients aged 65 and older, admitted to our health systems' hospitals with COVID-19 between March 1 and April 20, 2020. Presentation as reported by the OA or his/her representative is documented by the admitting professional and includes both symptoms and signs. Natural language processing was used to code the presence/absence of each symptom or sign. Typical presentation was defined as words indicating fever, cough, or shortness of breath; atypical presentation was defined as words indicating functional decline or altered mental status.

RESULTS

Of 4 961 unique OAs, atypical presentation characterized by functional decline or altered mental status was present in 24.9% and 11.3%, respectively. Atypical presentation was associated with older age, female gender, Black race, non-Hispanic ethnicity, higher comorbidity index, and the presence of dementia and diabetes mellitus. Those who presented typically were 1.39 times more likely than those who presented atypically to receive intensive care unit-level care. Hospital outcomes of mortality, length of stay, and 30-day readmission were similar between OAs with typical versus atypical presentations.

CONCLUSION

Although atypical presentation in OAs is not associated with the same need for acute intervention as respiratory distress, it must not be dismissed.

Comparison of Five Triage Tools for Identifying Mortality Risk and Injury Severity of Multiple Trauma Patients Admitted to the Emergency Department in the Daytime and Nighttime: A Retrospective Study

Effective triage tools are indispensable for doctors to make a prompt decision for the treatment of multiple trauma patients in emergency departments (EDs). The Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), standardized early warning score (SEWS), Modified Rapid Emergency Medicine Score (mREMS), and Revised Trauma Score (RTS) are five common triage tools proposed for trauma management. However, few studies have compared these tools in a multiple trauma cohort and investigated the influence of nighttime admission on the performance of these tools. This retrospective study was aimed at evaluating and comparing the performance of MEWS, NEWS, SEWS, mREMS, and RTS for identifying the mortality risk and trauma severity of patients with multiple trauma admitted to the ED during the daytime and nighttime. Retrospective data were collected from the medical records of patients with multiple trauma admitted in the daytime or nighttime to calculate scores for each triage tool. Logistic regression analysis was conducted on each triage tool for identifying in-hospital mortality and severe trauma (injury severity score > 15) in the daytime and nighttime. The performance of the tools was evaluated and compared by calculating area under the receiver operating characteristic curve (AUROC) of the retrospective logistic model of each tool. We collected data for 1,818 admissions, including 1,070 daytime and 748 nighttime admissions. A comparison of performance for identifying in-hospital mortality between daytime and nighttime yielded the following results (AUROC): MEWS (0.95 vs. 0.93, p = 0.384), NEWS (0.95 vs. 0.94, p = 0.708), SEWS (0.95 vs. 0.94, p = 0.683), mREMS (0.94 vs. 0.92, p = 0.286), and RTS (0.93 vs. 0.93, p = 0.87). Similarly, a comparison of performance for identifying trauma severity between daytime and nighttime yielded the following results (AUROC): MEWS (0.78 vs. 0.78, p = 0.95), NEWS (0.8 vs. 0.8, p = 0.885), SEWS (0.78 vs. 0.78, p = 0.818), mREMS (0.75 vs. 0.69, p = 0.019), and RTS (0.75 vs. 0.74, p = 0.619). All five scores are excellent triage tools (AUROC ≥ 0.9) for identifying in-hospital mortality for both daytime and nighttime admissions. However, they have only moderate effectiveness (AUROC < 0.9) at identifying severe trauma. The NEWS is the best triage tool for identifying severe trauma for both daytime and nighttime admissions. The MEWS, NEWS, SEWS, and RTS exhibited no significant differences in performance for identifying in-hospital mortality or severe trauma during the daytime or nighttime. However, the mREMS was better at identifying severe trauma during the daytime.

Factors Associated With Care Trajectory Following Acute Functional Decline in Older Home Nursing Care Patients: A Prospective Observational Study

Health policies and previous research highlight the importance of early identification and treatment of clinical deterioration in older patients to prevent frailty, higher levels of care, and mortality. This study explores older home nursing care patients’ care trajectories and factors associated with clinical response (type and level of intervention) from the health care services, final level of community care and death within 3 months after an incidence of acute functional decline. This observational study with a prospective, descriptive design includes a sample of 135 older home nursing care patients with acute functional decline. Demographic, health-related, and clinical characteristics were analyzed and prediction models for care trajectories were fitted using Bayesian generalized mixed models. Age ranged from 65 to 100, with a median age of 85. Hospital admission were registered for 13.33% ( T1) and 8.77% ( T2) of the participants. Nine patients (6.7%) were transferred to a higher level of community care, and 11 patients (8.1%) died. Frequent transitions between levels of care characterized care trajectories for patients experiencing more severe functional decline. Age, living in a private home, and increased Modified Early Warning Scores (MEWS) were associated with level of clinical responses throughout the care trajectory. Living in a private home was associated with the patients’ final level of community care. Female gender, hospital admission, and increased MEWS scores were associated with death. Health care personnel must be vigilant when MEWS scores rise even slightly, as this might be an indication of acute functional decline with possible increased risk of mortality.

Neurological Prognostic Factors in Hospitalized Patients with COVID-19

We aimed to search whether neurological symptoms or signs (NSS) and the MEWS (Modified Early Warning Score) score were associated with in-hospital mortality or oxygen requirement during the first 14 days of hospitalization in COVID-19 patients recruited at the University Hospital in Krakow, Poland. The detailed clinical questionnaires on twenty NSS were either filled out by patients prospectively or retrospectively assessed by neurologists based on daily medical records. NSS were considered high or low-risk if they were associated with increased or decreased mortality in the univariable analysis. This cohort study included 349 patients with COVID-19 (median age 64, interquartile range (51-77), women 54.72%). The presence of high-risk NSS (decreased level of consciousness, delirium, seizures, and symptoms of stroke or transient ischemic attack) or its combination with the absence of low-risk NSS (headache, dizziness, decreased mood, and fatigue) increased the risk of in-hospital mortality in SARS-CoV-2 infection 3.13 and 7.67-fold, respectively. The presence of low-risk NSS decreased the risk of in-hospital mortality in COVID-19 patients more than 6-fold. Death in patients with SARS-CoV-2 infection, apart from NSS, was predicted by older age, neoplasm, and higher MEWS scores on admission. High-risk NSS or their combination with the absence of low-risk NSS increased the risk of oxygen requirement during hospitalization in COVID-19 patients 4.48 and 1.86-fold, respectively. Independent predictors of oxygen therapy during hospitalization in patients with SARS-CoV-2 infection were also older age, male sex, neoplasm, and higher MEWS score on admission.

NUTRIC Score Is Not Superior to mNUTRIC Score in Prediction of Mortality of COVID-19 Patients

OBJECTIVES

The NUTRIC (nutrition risk in the critically ill) score and the modified NUTRIC score are two scoring systems that show the nutritional risk status and severity of acute disease of patients. The only difference between them is the examination of interleukin-6 (IL-6) level. The aim of this study was to investigate whether or not the NUTRIC score is superior to the mNUTRIC score in the prediction of mortality of patients with COVID-19 followed up in the Intensive Care Unit (ICU). Material and Method. This retrospective study included 322 patients followed up in ICU with a diagnosis of COVID-19. A record was made of demographic data, laboratory values, clinical results, and mortality status. All the data of the patients were compared between high and low variations of the NUTRIC score and the mNUTRIC score.

RESULTS

A high NUTRIC score was determined in 62 patients and a high mNUTRIC score in 86 patients. The need for invasive mechanical ventilation, the use of vasopressors in ICU, the development of acute kidney injury, and mortality rates were statistically significantly higher in the patients with high NUTRIC and high mNUTRIC scores than in those with low scores (p = 0.0001 for all). The AUC values were 0.791 for high NUTRIC score and 0.786 for high mNUTRIC score (p = 0.0001 for both). No statistically significant difference was determined between the two scoring systems.

CONCLUSION

Although the NUTRIC score was seen to be superior to the mNUTRIC score, no statistically significant difference was determined. Therefore, when IL-6 cannot be examined, the mNUTRIC score can be considered safe and effective for the prediction of mortality in COVID-19 patients.

Bioelectric impedance body composition and phase angle in relation to 90-day adverse outcome in hospitalized COVID-19 ward and ICU patients: The prospective BIAC-19 study

BACKGROUND & AIMS

Gaining insight into readily obtainable baseline characteristics that allow prediction of adverse outcome in COVID-19 aids both treatment and healthcare planning. Bioelectric impedance (BIA) Phase Angle (PhA) is correlated with outcome in a multitude of diseases and may be of added value in predicting adverse outcome of COVID-19. We aimed to associate baseline body composition parameters with 90-day adverse outcome of COVID-19 including ICU-admission and to explore the added predictive value of baseline PhA.

METHODS

We performed a prospective observational study, conducting BIA amongst COVID-19 patients within 24 hours of hospital admission, with a follow-up of 90 days. Data were compared between ward-only and ICU-patients. Regression models were used to assess the associations between baseline characteristics, body composition and 90-day adverse outcome, including a composite outcome score of morbidity, ICU-admission, and mortality. An ROC-curve was used to explore the added predictive value of PhA to other clinical parameters at baseline for the prediction of adverse outcome.

RESULTS

One-hundred-and-fifty patients were included. Mean age was 68 (66-70) years, 67% were male. Forty-one (27%) patients were admitted to ICU and 77 (51%) met the criteria of the composite outcome score. In multiple regression, PhA was independently, inversely correlated with risk of ICU-admission (OR .531, p = .021), complications (OR .579, p = .031), hospital length of stay (OR .875, p = .037) and the composite outcome score (OR .502, p = .012). An ROC-curve showed that the incorporation of PhA in a composite risk-score improved the discriminative power for the composite outcome from poor to fair, compared to individual predictors (AUC 0.79 (95% CI 0.71-0.87)).

CONCLUSION

BIA measurements including Phase Angle are independently correlated with an adverse outcome of COVID-19. Interpretation of Phase Angle can be a valuable addition to risk assessment of adverse outcome of COVID-19 at hospital admission.

CLINICAL TRIAL REGISTRATION

Netherlands Trial Register number NL8562, registered 2020-04-21.

Comparison of Different Scoring Systems for Prediction of Mortality and ICU Admission in Elderly CAP Population

Background

The incidence and mortality rate of community-acquired pneumonia (CAP) in elderly patients were higher than the younger population. Different scoring systems, including The quick Sequential Organ Function Assessment (qSOFA), Combination of Confusion, Urea, Respiratory Rate, Blood Pressure, and Age ≥65 (CURB-65), Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS), were used widely for predicting mortality and ICU admission of patients with community-acquired pneumonia (CAP). This study aimed to identify the most suitable score system for better hospitalization.

Methods

We retrospectively analyzed elderly patients with CAP in Minhang Hospital, Fudan University from 1 January 2018 to 1 January 2020. We recorded information of the patients including age, gender, underlying disease, consciousness state, vital signs, physiological and laboratory variables and further calculated the qSOFA, CURB-65, MEWS, and NEWS scores. Receiver operating characteristic (ROC) curves were used to predict the mortality risk and ICU admission. Kaplan–Meier survival curves were used in survival rate.

Results

In total, 1044 patients were selected for analysis and divided into two groups, namely survivor groups (902 cases) and non-survivor groups (142 cases). Depending on ICU admission enrolled patients were classified into ICU admission (n = 102) and non-ICU admission (n = 942) groups. Mortality expressed as AUC values were 0.844 (p < 0.001), 0.868 (p < 0.001), 0.927 (p < 0.001) and 0.892 (p < 0.001) for qSOFA, CURB 65, MEWS and NEWS, respectively. There were clear differences in MEWS vs CURB-65 (p < 0.0001), MEWS vs NEWS (p < 0.001), MEWS vs qSOFA (p < 0.0001). For ICU-admission, the AUC values of qSOFA, CURB-65, MEWS and NEWS scores were 0.866 (p < 0.001), 0.854 (p < 0.001), 0.922 (p < 0.001), 0.976 (p < 0.001), respectively. There were significant differences in NEWS vs CURB-65 (p < 0.0001), NEWS vs MEWS (p < 0.001), NEWS vs qSOFA (p < 0.0001).

Conclusion

We explored the outcome prediction values of CURB65, qSOFA, MEWS and NEWS for patients aged 65-years and older with community-acquired pneumonia. We found that MEWS showed superiority over the other severity scores in predicting hospital mortality, and NEWS showed superiority over the other scores in predicting ICU admission.

Identification and validation of prognostic factors in patients with COVID-19: A retrospective study based on artificial intelligence algorithms

Background

Novel coronavirus disease 2019 (COVID-19) is an ongoing global pandemic with high mortality. Although several studies have reported different risk factors for mortality in patients based on traditional analytics, few studies have used artificial intelligence (AI) algorithms. This study investigated prognostic factors for COVID-19 patients using AI methods.

Methods

COVID-19 patients who were admitted in Wuhan Infectious Diseases Hospital from December 29, 2019 to March 2, 2020 were included. The whole cohort was randomly divided into training and testing sets at a 6:4 ratio. Demographic and clinical data were analyzed to identify predictors of mortality using least absolute shrinkage and selection operator (LASSO) regression and LASSO-based artificial neural network (ANN) models. The predictive performance of the models was evaluated using receiver operating characteristic (ROC) curve analysis.

Results

A total of 1145 patients (610 male, 53.3%) were included in the study. Of the 1145 patients, 704 were assigned to the training set and 441 were assigned to the testing set. The median age of the patients was 57 years (range: 47-66 years). Severity of illness, age, platelet count, leukocyte count, prealbumin, C-reactive protein (CRP), total bilirubin, Acute Physiology and Chronic Health Evaluation (APACHE) II score, and Sequential Organ Failure Assessment (SOFA) score were identified as independent prognostic factors for mortality. Incorporating these nine factors into the LASSO regression model yielded a correct classification rate of 0.98, with area under the ROC curve (AUC) values of 0.980 and 0.990 in the training and testing cohorts, respectively. Incorporating the same factors into the LASSO-based ANN model yielded a correct classification rate of 0.990, with an AUC of 0.980 in both the training and testing cohorts.

Conclusions

Both the LASSO regression and LASSO-based ANN model accurately predicted the clinical outcome of patients with COVID-19. Severity of illness, age, platelet count, leukocyte count, prealbumin, CRP, total bilirubin, APACHE II score, and SOFA score were identified as prognostic factors for mortality in patients with COVID-19.

Evaluating risk stratification scoring systems to predict mortality in patients with COVID-19

Background

The COVID-19 pandemic has necessitated efficient and accurate triaging of patients for more effective allocation of resources and treatment.

Objectives

The objectives are to investigate parameters and risk stratification tools that can be applied to predict mortality within 90 days of hospital admission in patients with COVID-19.

Methods

A literature search of original studies assessing systems and parameters predicting mortality of patients with COVID-19 was conducted using MEDLINE and EMBASE.

Results

589 titles were screened, and 76 studies were found investigating the prognostic ability of 16 existing scoring systems (area under the receiving operator curve (AUROC) range: 0.550–0.966), 38 newly developed COVID-19-specific prognostic systems (AUROC range: 0.6400–0.9940), 15 artificial intelligence (AI) models (AUROC range: 0.840–0.955) and 16 studies on novel blood parameters and imaging.

Discussion

Current scoring systems generally underestimate mortality, with the highest AUROC values found for APACHE II and the lowest for SMART-COP. Systems featuring heavier weighting on respiratory parameters were more predictive than those assessing other systems. Cardiac biomarkers and CT chest scans were the most commonly studied novel parameters and were independently associated with mortality, suggesting potential for implementation into model development. All types of AI modelling systems showed high abilities to predict mortality, although none had notably higher AUROC values than COVID-19-specific prediction models. All models were found to have bias, including lack of prospective studies, small sample sizes, single-centre data collection and lack of external validation.

Conclusion

The single parameters established within this review would be useful to look at in future prognostic models in terms of the predictive capacity their combined effect may harness.

The role of emergency department triage early warning score (TREWS) and modified early warning score (MEWS) to predict in-hospital mortality in COVID-19 patients

Background

It is necessary to identify critical patients requiring hospitalization early due to the rapid increase in the number of COVID-19 cases.

Aim

This study aims to evaluate the effectiveness of scoring systems such as emergency department triage early warning score (TREWS) and modified early warning score (MEWS) in predicting mortality in COVID-19 patients.

Methods

In this retrospective cohort study, PCR positive patients evaluated for COVID-19 and decided to be hospitalized were evaluated. During the first evaluation, MEWS and TREWS scores of the patients were calculated. Intensive care needs as well as 24-h and 28-day mortality rates were evaluated.

Results

A total of 339 patients were included in the study. While 30 (8.8%) patients were hospitalized in the intensive care unit, 4 (1.2%) died in the emergency. The number of patients who died within 28 days was found to be 57 (16.8%). In 24-h mortality, the median MEWS value was found to be 7 (IQR 25–75) while the TREWS value was 11.5 (IQR 25–75). In the ROC analysis made for the diagnostic value of 28-day mortality of MEWS and TREWS scores, the area under the curve (AUC) for the MEWS score was found to be 0.833 (95% CI 0.777–0.888, p < 0.001) while it was identified as 0.823 (95% CI 0.764–0.882, p < 0.001) for the TREWS.

Conclusion

MEWS and TREWS calculated at emergency services are effective in predicting 28-day mortality in patients requiring hospitalization due to COVID-19.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE

To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease.

DESIGN

Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.

DATA SOURCES

PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.

STUDY SELECTION

Studies that developed or validated a multivariable covid-19 related prediction model.

DATA EXTRACTION

At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).

RESULTS

37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models.

CONCLUSION

Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.

SYSTEMATIC REVIEW REGISTRATION

Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.

READERS' NOTE

This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.