Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values

Identification of predictive patient characteristics for assessing the probability of COVID-19 in-hospital mortality

As the world emerges from the COVID-19 pandemic, there is an urgent need to understand patient factors that may be used to predict the occurrence of severe cases and patient mortality. Approximately 20% of SARS-CoV-2 infections lead to acute respiratory distress syndrome caused by the harmful actions of inflammatory mediators. Patients with severe COVID-19 are often afflicted with neurologic symptoms, and individuals with pre-existing neurodegenerative disease have an increased risk of severe COVID-19. Although collectively, these observations point to a bidirectional relationship between severe COVID-19 and neurologic disorders, little is known about the underlying mechanisms. Here, we analyzed the electronic health records of 471 patients with severe COVID-19 to identify clinical characteristics most predictive of mortality. Feature discovery was conducted by training a regularized logistic regression classifier that serves as a machine-learning model with an embedded feature selection capability. SHAP analysis using the trained classifier revealed that a small ensemble of readily observable clinical features, including characteristics associated with cognitive impairment, could predict in-hospital mortality with an accuracy greater than 0.85 (expressed as the area under the ROC curve of the classifier). These findings have important implications for the prioritization of clinical measures used to identify patients with COVID-19 (and, potentially, other forms of acute respiratory distress syndrome) having an elevated risk of death.

Risk factors for poor COVID-19 outcomes in patients with psychiatric disorders

BACKGROUND

Coronavirus disease 2019 (COVID-19) has been found to have a greater impact on individuals with pre-existing psychiatric disorders. However, the underlying reasons for this increased risk have yet to be determined. This study aims to investigate the potential factors contributing poor outcomes among COVID-19 patients with psychiatric disorders, including delayed diagnosis of infection, vaccination rates, immune response, and the use of psychotropic medications.

METHODS

This retrospective cohort study analyzed medical records of 15,783 adult patients who were diagnosed with COVID-19 infection by positive PCR tests between January and September 2022 at a single medical center. We identified psychiatric diagnoses using ICD-9 diagnostic codes from the preceding 3 years before COVID infection. Primary outcome was in-hospital mortality and secondary outcomes were severe illness requiring intensive care or mechanical ventilation, and hospitalization within 45 days after a positive COVID-19 test. We compared the rates of outcomes, viral load, vaccination status at the time of positive test, psychotropic medications prescription within 90 days prior, antiviral medication use, and blood inflammation markers between patients with and without psychiatric disorders. The Cox proportional hazard model was used to examine the association of psychiatric diagnoses, vaccination status, and psychotropic medication prescription with poor outcomes.

RESULTS

Patients with psychiatric disorders demonstrated higher rates of severe illness (10.4% v.s. 7.1%) and hospitalization (16.4% vs. 11.3%), as well as a shorter duration to in-hospital mortality (6 vs. 12.5 days) compared to non-psychiatric patients. Psychiatric patients had higher vaccination rates and lower levels of inflammatory markers than non-psychiatric patients. Antipsychotic medication use was associated with in-hospital mortality (hazard ratio [HR] = 4.79, 95% confidence interval [CI] = 1.23-18.7), while being unvaccinated was associated with hospitalization (HR = 1.81, 95% CI = 1.29 to 2.54) and severe illness (HR = 3.23, 95% CI = 1.95 to 5.34) among patients with psychiatric disorders. Sedatives prescription was associated with all poor outcomes in general patients.

CONCLUSION

Considering the narrow time window between a positive COVID-19 test and poor outcomes, healthcare providers should undertake close monitoring of patients with preexisting psychiatric disorders during the initial days after a positive PCR test. Furthermore, caution should be taken when prescribing psychotropic medications, with special attention to antipsychotics.

COVID-19 risk, course and outcome in people with mental disorders: a systematic review and meta-analyses

AIMS

It has been suggested that people with mental disorders have an elevated risk to acquire severe acute respiratory syndrome coronavirus 2 and to be disproportionally affected by coronavirus disease 19 (COVID-19) once infected. We aimed to analyse the COVID-19 infection rate, course and outcome, including mortality and long COVID, in people with anxiety, depressive, neurodevelopmental, schizophrenia spectrum and substance use disorders relative to control subjects without these disorders.

METHODS

This study constitutes a preregistered systematic review and random-effects frequentist and Bayesian meta-analyses. Major databases were searched up until 27 June 2023.

RESULTS

Eighty-one original articles were included reporting 304 cross-sectional and prospective effect size estimates (median n per effect-size = 114837) regarding associations of interest. Infection risk was not significantly increased for any mental disorder that we investigated relative to samples of people without these disorders. The course of COVID-19, however, is relatively severe, and long COVID and COVID-19-related hospitalization are more likely in all patient samples that we investigated. The odds of dying from COVID-19 were high in people with most types of mental disorders, except for those with anxiety and neurodevelopmental disorders relative to non-patient samples (pooled ORs range, 1.26-2.57). Bayesian analyses confirmed the findings from the frequentist approach and complemented them with estimates of the strength of evidence.

CONCLUSIONS

Once infected, people with pre-existing mental disorders are at an elevated risk for a severe COVID-19 course and outcome, including long COVID and mortality, relative to people without pre-existing mental disorders, despite an infection risk not significantly increased.

Obesity as an independent risk factor for COVID-19 severity and mortality

BACKGROUND

Since December 2019, the world has struggled with the COVID-19 pandemic. Even after the introduction of various vaccines, this disease still takes a considerable toll. In order to improve the optimal allocation of resources and communication of prognosis, healthcare providers and patients need an accurate understanding of factors (such as obesity) that are associated with a higher risk of adverse outcomes from the COVID-19 infection.

OBJECTIVES

To evaluate obesity as an independent prognostic factor for COVID-19 severity and mortality among adult patients in whom infection with the COVID-19 virus is confirmed.

SEARCH METHODS

MEDLINE, Embase, two COVID-19 reference collections, and four Chinese biomedical databases were searched up to April 2021.

SELECTION CRITERIA

We included case-control, case-series, prospective and retrospective cohort studies, and secondary analyses of randomised controlled trials if they evaluated associations between obesity and COVID-19 adverse outcomes including mortality, mechanical ventilation, intensive care unit (ICU) admission, hospitalisation, severe COVID, and COVID pneumonia. Given our interest in ascertaining the independent association between obesity and these outcomes, we selected studies that adjusted for at least one factor other than obesity. Studies were evaluated for inclusion by two independent reviewers working in duplicate.  DATA COLLECTION AND ANALYSIS: Using standardised data extraction forms, we extracted relevant information from the included studies. When appropriate, we pooled the estimates of association across studies with the use of random-effects meta-analyses. The Quality in Prognostic Studies (QUIPS) tool provided the platform for assessing the risk of bias across each included study. In our main comparison, we conducted meta-analyses for each obesity class separately. We also meta-analysed unclassified obesity and obesity as a continuous variable (5 kg/m2 increase in BMI (body mass index)). We used the GRADE framework to rate our certainty in the importance of the association observed between obesity and each outcome. As obesity is closely associated with other comorbidities, we decided to prespecify the minimum adjustment set of variables including age, sex, diabetes, hypertension, and cardiovascular disease for subgroup analysis.  MAIN RESULTS: We identified 171 studies, 149 of which were included in meta-analyses.  As compared to 'normal' BMI (18.5 to 24.9 kg/m2) or patients without obesity, those with obesity classes I (BMI 30 to 35 kg/m2), and II (BMI 35 to 40 kg/m2) were not at increased odds for mortality (Class I: odds ratio [OR] 1.04, 95% confidence interval [CI] 0.94 to 1.16, high certainty (15 studies, 335,209 participants); Class II: OR 1.16, 95% CI 0.99 to 1.36, high certainty (11 studies, 317,925 participants)). However, those with class III obesity (BMI 40 kg/m2 and above) may be at increased odds for mortality (Class III: OR 1.67, 95% CI 1.39 to 2.00, low certainty, (19 studies, 354,967 participants)) compared to normal BMI or patients without obesity. For mechanical ventilation, we observed increasing odds with higher classes of obesity in comparison to normal BMI or patients without obesity (class I: OR 1.38, 95% CI 1.20 to 1.59, 10 studies, 187,895 participants, moderate certainty; class II: OR 1.67, 95% CI 1.42 to 1.96, 6 studies, 171,149 participants, high certainty; class III: OR 2.17, 95% CI 1.59 to 2.97, 12 studies, 174,520 participants, high certainty). However, we did not observe a dose-response relationship across increasing obesity classifications for ICU admission and hospitalisation.

AUTHORS' CONCLUSIONS

Our findings suggest that obesity is an important independent prognostic factor in the setting of COVID-19. Consideration of obesity may inform the optimal management and allocation of limited resources in the care of COVID-19 patients.

Comorbid asthma decreased the risk for COVID-19 mortality in the United Kingdom: Evidence based on a meta-analysis

The study aimed to investigate the influence of comorbid asthma on the risk for mortality among patients with coronavirus disease 2019 (COVID-19) in the United Kingdom (UK) by utilizing a quantitative meta-analysis. The pooled odds ratio (OR) with 95% confidence interval (CI) was estimated by conducting a random-effects model. Sensitivity analysis, I² statistic, meta-regression, subgroup analysis, Begg's analysis and Egger's analysis were all implemented. Our results presented that comorbid asthma was significantly related to a decreased risk for COVID-19 mortality in the UK based on 24 eligible studies with 1,209,675 COVID-19 patients (pooled OR = 0.81, 95% CI: 0.71-0.93; I² = 89.2%, P < 0.01). Coming through further meta-regression to seek the possible cause of heterogeneity, none of elements might be responsible for heterogeneity. A sensitivity analysis proved the stability and reliability of the overall results. Both Begg's analysis (P = 1.000) and Egger's analysis (P = 0.271) manifested that publication bias did not exist. In conclusion, our data demonstrated that COVID-19 patients with comorbid asthma might bear a lower risk for mortality in the UK. Furthermore, routine intervention and treatment of asthma patients with severe acute respiratory syndrome coronavirus 2 infection should be continued in the UK.

Informing antimicrobial stewardship with explainable AI

The accuracy and flexibility of artificial intelligence (AI) systems often comes at the cost of a decreased ability to offer an intuitive explanation of their predictions. This hinders trust and discourage adoption of AI in healthcare, exacerbated by concerns over liabilities and risks to patients' health in case of misdiagnosis. Providing an explanation for a model's prediction is possible due to recent advances in the field of interpretable machine learning. We considered a data set of hospital admissions linked to records of antibiotic prescriptions and susceptibilities of bacterial isolates. An appropriately trained gradient boosted decision tree algorithm, supplemented by a Shapley explanation model, predicts the likely antimicrobial drug resistance, with the odds of resistance informed by characteristics of the patient, admission data, and historical drug treatments and culture test results. Applying this AI-based system, we found that it substantially reduces the risk of mismatched treatment compared with the observed prescriptions. The Shapley values provide an intuitive association between observations/data and outcomes; the associations identified are broadly consistent with expectations based on prior knowledge from health specialists. The results, and the ability to attribute confidence and explanations, support the wider adoption of AI in healthcare.

Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes

The clinical outcome of SARS-CoV-2 infection varies widely between individuals. Machine learning models can support decision making in healthcare by assessing fatality risk in patients that do not yet show severe signs of COVID-19. Most predictive models rely on static demographic features and clinical values obtained upon hospitalization. However, time-dependent biomarkers associated with COVID-19 severity, such as antibody titers, can substantially contribute to the development of more accurate outcome models. Here we show that models trained on immune biomarkers, longitudinally monitored throughout hospitalization, predicted mortality and were more accurate than models based on demographic and clinical data upon hospital admission. Our best-performing predictive models were based on the temporal analysis of anti-SARS-CoV-2 Spike IgG titers, white blood cell (WBC), neutrophil and lymphocyte counts. These biomarkers, together with C-reactive protein and blood urea nitrogen levels, were found to correlate with severity of disease and mortality in a time-dependent manner. Shapley additive explanations of our model revealed the higher predictive value of day post-symptom onset (PSO) as hospitalization progresses and showed how immune biomarkers contribute to predict mortality. In sum, we demonstrate that the kinetics of immune biomarkers can inform clinical models to serve as a powerful monitoring tool for predicting fatality risk in hospitalized COVID-19 patients, underscoring the importance of contextualizing clinical parameters according to their time post-symptom onset.