Daily surveillance with early warning scores help predicthospital mortality in medical wards

Outcome of noncritical COVID-19 patients with early hospitalization and early antiviral treatment outside the ICU

Background/aim

Despite the fact that the COVID-19 pandemic has been going on for over 5 months, there is yet to be a standard management policy for all patients including those with mild-to-moderate cases. We evaluated the role of early hospitalization in combination with early antiviral therapy with COVID-19 patients in a tertiary care university hospital.

Materials and methods

This was a prospective, observational, single-center study on probable/confirmed COVID-19 patients hospitalized in a tertiary care hospital on COVID-19 wards between March 20 and April 30, 2020. The demographic, laboratory, and clinical data were collected.

Results

We included 174 consecutive probable/confirmed COVID-19 adult patients hospitalized in the Internal Medicine wards of the University Adult Hospital between March 20 and April 30, 2020. The median age was 45.5 (19–92) years and 91 patients (52.3%) were male. One hundred and twenty (69%) were confirmed microbiologically, 41 (23.5%) were radiologically diagnosed, and 13 (7.5%) were clinically suspected (negative microbiological and radiological findings compatible with COVID-19); 35 (20.1%) had mild, 107 (61.5%) moderate disease, and 32 (18.4%) had severe pneumonia. Out of 171 cases, 130 (74.3%) showed pneumonia; 80 were typical, and 50 showed indeterminate infiltration for COVID-19. Patients were admitted within a median of 3 days (0-14 days) after symptoms appear. The median duration of hospitalization was 4 days (0-28 days). In this case series, 13.2% patients were treated with hydroxychloroquine alone, 64.9% with hydroxychloroquine plus azithromycin, and 18.4% with regimens including favipiravir. A total of 15 patients (8.5%) were transferred to the ICU. Four patients died (2.2%).

Conclusion

In our series, 174 patients were admitted to the hospital wards for COVID-19, 69% were confirmed with PCR and/or antibody test. At the time of admission, nearly one fifth of the patients had severe diseases. Of the patients, 95.4% received hydroxychloroquine alone or in combination. The overall case fatality rate was 2.2%.

Evaluation of the blue code system established in the health campus of a university hospital

OBJECTIVE

We report the hospital outcomes after implementing the blue code system in our hospital and health campus. We also aimed to determine factors related to mortality.

METHODS

This is a retrospective observational study of the patients who received cardiopulmonary resuscitation (CPR). All blue code calls for all age groups between March 15, 2013, and April 30, 2015 were analyzed. Logistic regression analysis was performed to find independent predictors of in-hospital mortality.

RESULTS

A total of 155 patients from the blue code calls were evaluated. Return of spontaneous circulation was achieved in 45.5% of patients, and 54.8% of the patients had died at the end of the CPR. The hospital discharge rate was 20%. Of all patients, 65% were adults with a survival rate of 7.9%, whereas pediatric patients had a 44.2% survival rate. Asystole and pulseless electrical activity were the predominant electrocardiography rhythms in 92.4% of patients. The comparison of survivors and nonsurvivors revealed that nonsurvivors were older, had more cancer as the comorbidity, had a more cardiac arrest, and sepsis as the underlying cause and had >20 min of CPR. The logistic regression analysis demonstrated the independent risk factors for mortality as arrest at a hospital ward, and sepsis as the underlying cause and being adult patient.

CONCLUSION

The performance of the blue code system should be evaluated periodically. Every effort should be made to prevent unexpected cardiac arrests and increase hospital discharge with good neurologic outcomes.

Early warning score validation methodologies and performance metrics: a systematic review

Background

Early warning scores (EWS) have been developed as clinical prognostication tools to identify acutely deteriorating patients. In the past few years, there has been a proliferation of studies that describe the development and validation of novel machine learning-based EWS. Systematic reviews of published studies which focus on evaluating performance of both well-established and novel EWS have shown conflicting conclusions. A possible reason is the heterogeneity in validation methods applied. In this review, we aim to examine the methodologies and metrics used in studies which perform EWS validation.

Methods

A systematic review of all eligible studies from the MEDLINE database and other sources, was performed. Studies were eligible if they performed validation on at least one EWS and reported associations between EWS scores and inpatient mortality, intensive care unit (ICU) transfers, or cardiac arrest (CA) of adults. Two reviewers independently did a full-text review and performed data abstraction by using standardized data-worksheet based on the TRIPOD (Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) checklist. Meta-analysis was not performed due to heterogeneity.

Results

The key differences in validation methodologies identified were (1) validation dataset used, (2) outcomes of interest, (3) case definition, time of EWS use and aggregation methods, and (4) handling of missing values. In terms of case definition, among the 48 eligible studies, 34 used the patient episode case definition while 12 used the observation set case definition, and 2 did the validation using both case definitions. Of those that used the patient episode case definition, 18 studies validated the EWS at a single point of time, mostly using the first recorded observation. The review also found more than 10 different performance metrics reported among the studies.

Conclusions

Methodologies and performance metrics used in studies performing validation on EWS were heterogeneous hence making it difficult to interpret and compare EWS performance. Standardizing EWS validation methodology and reporting can potentially address this issue.

Developing a framework for evidence-based grading and assessment of predictive tools for clinical decision support

BACKGROUND

Clinical predictive tools quantify contributions of relevant patient characteristics to derive likelihood of diseases or predict clinical outcomes. When selecting predictive tools for implementation at clinical practice or for recommendation in clinical guidelines, clinicians are challenged with an overwhelming and ever-growing number of tools, most of which have never been implemented or assessed for comparative effectiveness. To overcome this challenge, we have developed a conceptual framework to Grade and Assess Predictive tools (GRASP) that can provide clinicians with a standardised, evidence-based system to support their search for and selection of efficient tools.

METHODS

A focused review of the literature was conducted to extract criteria along which tools should be evaluated. An initial framework was designed and applied to assess and grade five tools: LACE Index, Centor Score, Well's Criteria, Modified Early Warning Score, and Ottawa knee rule. After peer review, by six expert clinicians and healthcare researchers, the framework and the grading of the tools were updated.

RESULTS

GRASP framework grades predictive tools based on published evidence across three dimensions: 1) Phase of evaluation; 2) Level of evidence; and 3) Direction of evidence. The final grade of a tool is based on the highest phase of evaluation, supported by the highest level of positive evidence, or mixed evidence that supports a positive conclusion. Ottawa knee rule had the highest grade since it has demonstrated positive post-implementation impact on healthcare. LACE Index had the lowest grade, having demonstrated only pre-implementation positive predictive performance.

CONCLUSION

GRASP framework builds on widely accepted concepts to provide standardised assessment and evidence-based grading of predictive tools. Unlike other methods, GRASP is based on the critical appraisal of published evidence reporting the tools' predictive performance before implementation, potential effect and usability during implementation, and their post-implementation impact. Implementing the GRASP framework as an online platform can enable clinicians and guideline developers to access standardised and structured reported evidence of existing predictive tools. However, keeping GRASP reports up-to-date would require updating tools' assessments and grades when new evidence becomes available, which can only be done efficiently by employing semi-automated methods for searching and processing the incoming information.

Comparison of Reverse Triage with National Early Warning Score, Sequential Organ Failure Assessment and Charlson Comorbidity Index to classify medical inpatients of an Italian II level hospital according to their resource's need

Resource allocation in our overcrowded hospitals would require classification of inpatients according to the severity of illness, the evolving risk and the clinical complexity. Reverse triage (RT) is a method used in disasters to identify inpatients according to their use of hospital resources. The aim of this observational prospective study is to evaluate the use of RT in medical inpatients of an Italian Hospital and to compare the RT score with National Early Warning Score, Sequential Organ Failure Assessment and Charlson Comorbidity Index. Cluster sampling was performed on high dependency unit (HDU), geriatrics (Ger) and internal medicine (IM) wards. We calculate RT, NEWS, SOFA and CCI from inpatient charts. Length of stay (LOS), transfer to a higher level of care, death and discharge date were collected after 30 days. We obtained demographics, comorbidities, severity and clinical complexity of 260 inpatients. We highlighted differences in NEWS, SOFA and CCI in the three divisions. On the contrary RT score was uniformly high (median 7), with 85% of patients with RT = 8. NEWS, SOFA and CCI were higher in patients with higher RT score. We used the sum of the interventions listed by RT (RT sum) as a proxy of the level of care needed. RT-sum showed moderate correlation with NEWS (r = 0.52 Spearman, p < 0.001). RT-sum was the highest in HDU, related to the evolving severity of HDU patients. Ger patients that showed the highest CCI score (with all patients in the CCI ≥ 3 category) had the second highest RT-sum. RT score showed similar values in the majority of the inpatients regardless of differences in NEWS, SOFA and CCI in different ward subgroups. RT-sum is related both to evolving severity (NEWS) and to clinical complexity (CCI). RT and NEWS could predict inpatient level of care and resource need associated with CCI.