Prediction of in-hospital mortality of patients with SARS-CoV-2 infection by comorbidity indexes: an Italian internal medicine single center study

Demographics, clinical characteristics, and outcomes in hospitalized patients during six waves of COVID‑19 in Northern Iran: a large cohort study

Since the first report of coronavirus disease 2019 (COVID-19) in Iran, our country has experienced several waves of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Northern Iran was one of the most affected regions of the country by COVID-19. In the current study, the demographic and clinical characteristics and outcomes of hospitalized patients were determined over a 2-year period (during six waves of SARS-CoV-2). This is a large cohort study investigating hospitalized patients with suspected and probable, and confirmed SARS-CoV-2 infection in Babol district, northern Iran, during the two years of COVID-19. The study population included patients admitted to four hospitals affiliated with Babol University of Medical Sciences between March 7, 2020 (start of the first wave) and March 20, 2022 (end of the sixth wave). Epidemiological and demographic characteristics, real-time PCR, cycle thresholds, clinical data and outcomes of COVID-19 were analyzed in 24,287 hospitalized patients. A total of 24,287 hospitalized patients were included in the study: 13,250 (46.6%) patients were suspected of having COVID-19, 11037(45.4%) were confirmed COVID-19 cases. The mean age of confirmed COVID-19 patients was 54.5 ± 18.9 years and 5961 (54%) were female. The median length of hospitalization for COVID-19 survivors and non-survivors was 5 (interquartile range [IQR] 4-8) and 7 (IQR 3-15) days, respectively. Of the patients with confirmed COVID-19, 714 (6.5%) died during hospitalization. In addition, the mortality rate from the first to the sixth wave was 22.9%, 8.1%, 9.9%, 6.8%, 2.7% and 3.5% in confirmed COVID-19 patients. The patients in the fifth wave were significantly younger than the others (mean age and SD of 51.1 ± 17.4 versus 59.2 ± 16.9, 54.7 ± 19.9, 58.4 ± 17.9, 53.5 ± 16.8 and 58.5 ± 25.1 years; p<0.001). The highest in-hospital mortality rate was 22.9% (126/551) in the first wave and the lowest in the fifth wave was 2.7% (96/3573) of cases. In conclusion, in the present study, the in-hospital mortality rate was 6.5% and more than half of the deceased patients were ≥65 years old. Male gender, advanced age and comorbidities significantly increased the mortality rate. The patients in the fifth wave were significantly younger than those in the other waves, and the lowest mortality rate and intensive care unit admission were also observed in the fifth wave.

Effect of Androgen-Androgen Receptor Directed Therapy on COVID-19 Outcome in Prostate Cancer Patients

TMPRSS2 is utilized by SARS-CoV-2 for cellular entry. Androgen-Androgen receptor directed therapy (A/ARDT) downregulates expression of TMPRSS2. We hypothesized A/ARDT might protect prostate cancer (PCa) patients from poor COVID-19 outcome. A retrospective analysis of PCa patients with COVID-19 infection was performed. 146 PCa cases were identified, 17% were on A/ARDT. Hospitalization rates were same 52% (OR = 0.99, 0.41-2.24). Mean hospitalization was 9.2 (Range: 1-25) and 14.9 (Range: 2-47) days in A/ARDT and non-A/ARDT groups, respectively. While definitive conclusions cannot be made regarding outcome differences between groups due to lack of statistical significance, these data generate hypothesis that A/ARDT might shorten hospitalization stay.

Proteomic analysis of cardiometabolic biomarkers and predictive modeling of severe outcomes in patients hospitalized with COVID-19

BACKGROUND

The high heterogeneity in the symptoms and severity of COVID-19 makes it challenging to identify high-risk patients early in the disease. Cardiometabolic comorbidities have shown strong associations with COVID-19 severity in epidemiologic studies. Cardiometabolic protein biomarkers, therefore, may provide predictive insight regarding which patients are most susceptible to severe illness from COVID-19.

METHODS

In plasma samples collected from 343 patients hospitalized with COVID-19 during the first wave of the pandemic, we measured 92 circulating protein biomarkers previously implicated in cardiometabolic disease. We performed proteomic analysis and developed predictive models for severe outcomes. We then used these models to predict the outcomes of out-of-sample patients hospitalized with COVID-19 later in the surge (N = 194).

RESULTS

We identified a set of seven protein biomarkers predictive of admission to the intensive care unit and/or death (ICU/death) within 28 days of presentation to care. Two of the biomarkers, ADAMTS13 and VEGFD, were associated with a lower risk of ICU/death. The remaining biomarkers, ACE2, IL-1RA, IL6, KIM1, and CTSL1, were associated with higher risk. When used to predict the outcomes of the future, out-of-sample patients, the predictive models built with these protein biomarkers outperformed all models built from standard clinical data, including known COVID-19 risk factors.

CONCLUSIONS

These findings suggest that proteomic profiling can inform the early clinical impression of a patient's likelihood of developing severe COVID-19 outcomes and, ultimately, accelerate the recognition and treatment of high-risk patients.

Prognostic Role of Anemia in COVID-19 Patients: A Meta-Analysis

INTRODUCTION

The prevalence and prognostic implications of anemia in patients infected by the SARS-CoV-2 remains unclear. We performed a systematic review and meta-analysis to assess the prevalence and mortality risk in COVID-19 patients with anemia.

METHODS

Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed in abstracting data and assessing validity. We searched MEDLINE and Scopus to locate all the articles published up to 1 September 2021, reporting data on the adjusted OR (aOR) for mortality among COVID-19 patients with anemia. The pooled prevalence of anemia among COVID-19 patients was calculated using a random effects model and presenting the related 95% confidence interval (CI), while the mortality risk was estimated using the Mantel-Haenszel random effects models with odds ratio (aOR) and related 95% CI. Statistical heterogeneity was measured using the Higgins I² statistic.

RESULTS

Five studies, enrolling 9.623 COVID-19 patients [3.707 males (38.5%)], met the inclusion criteria and were included in the final analysis. The pooled prevalence of anemia was 25.6% of cases (95% CI: 8.3-56.5%), with high heterogeneity (I² = 98.9%). Meta-regression showed that the anemia prevalence was influenced by a direct correlation with age (p = 0.007) and chronic kidney disease (p = 0.004) as moderating variables. Conversely, an inverse relationship was observed with male gender (p < 0.0001). Anemia was significantly associated with higher risk of short-term mortality (aOR: 1.69, 95% CI: 1.28-2.24, p < 0.001), with low heterogeneity (I² = 0%).

CONCLUSIONS

Anemia represents a major comorbidity in about 25% of COVID-19 patients and it is associated with about 70% higher risk of short-term mortality.

Prognostic Role of Metabolic Syndrome in COVID-19 Patients: A Systematic Review Meta-Analysis

Background: The prevalence and prognostic implications of metabolic syndrome (MetS) in patients infected by the SARS-CoV-2 remain unclear. We performed a systematic review and meta-analysis of prevalence and mortality risk in COVID-19 patients with MetS. Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed in abstracting data and assessing validity. We searched MEDLINE and Scopus to locate every article published up to 1 September 2021, reporting data on MetS among COVID-19 patients. The pooled prevalence of MetS was calculated using a random effects model and presented using the related 95% confidence interval (CI), while the mortality risk was estimated using the Mantel-Haenszel random effects models with odds ratio (OR) and related 95% CI. Statistical heterogeneity was measured using the Higgins I² statistic. Results: Six studies, enrolling 209.569 COVID-19 patients [mean age 57.2 years, 114.188 males (54.4%)] met the inclusion criteria and were included in the final analysis. The pooled prevalence of dyslipidaemia was 20.5% of cases (95% CI: 6.7–47.8%, p = 0.03), with high heterogeneity (I² = 98.9%). Pre-existing MetS was significantly associated with higher risk of short-term mortality (OR: 2.30, 95% CI: 1.52–3.45, p < 0.001), with high heterogeneity (I² = 89.4%). Meta-regression showed a direct correlation with male gender (p = 0.03), hypertension (p < 0.001), DM (p = 0.01) and hyperlipidaemia (p = 0.04), but no effect when considering age (p = 0.75) and chronic pulmonary disease (p = 0.86) as moderators. Conclusions: MetS represents a major comorbidity in about 20% of COVID-19 patients and it is associated with a 230% increased risk of short-term mortality.

Pre-existing atrial fibrillation is associated with increased mortality in COVID-19 Patients

Purpose

The impacts of pre-existing atrial fibrillation (AF) on COVID-19-associated outcomes are unclear. We conducted a systematic review and meta-analysis to investigate the pooled prevalence of pre-existing AF and its short-term mortality risk in COVID-19 patients.

Methods

Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed in abstracting data and assessing validity. We searched MEDLINE and Scopus to locate all the articles published up to January 31, 2021, reporting data on pre-existing AF among COVID-19 survivors and non-survivors. The pooled prevalence of pre-existing AF was calculated using a random effects model and presenting the related 95% confidence interval (CI), while the mortality risk was estimated using the Mantel-Haenszel random effects models with odds ratio (OR) and related 95% CI. Statistical heterogeneity was measured using the Higgins I² statistic.

Results

Twelve studies, enrolling 15.562 COVID-19 patients (mean age 71.6 years), met the inclusion criteria and were included in the final analysis. The pooled prevalence of pre-existing AF was 11.0% of cases (95% CI: 7.8–15.2%, p < 0.0001) with high heterogeneity (I² = 95.2%). Pre-existing AF was associated with higher risk of short-term death (OR 2.22, 95% CI 1.47–3.36, p < 0.0001), with high heterogeneity (I² = 79.1%).

Conclusion

Pre-existing AF is present in about 11% of COVID-19 cases but results associated with an increased risk of short-term mortality.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10840-021-00992-2.

Do Sex-Related Differences of Comorbidity Burden and/or In-Hospital Mortality Exist in Cancer Patients? A Retrospective Study in an Internal Medicine Setting

Cancer represents important comorbidity, and data on outcomes are usually derived from selected oncologic units. Our aim was to evaluate possible sex-related differences and factors associated with in-hospital mortality (IHM) in a consecutive cohort of elderly patients with cancer admitted to internal medicine. We included all patients admitted to our department with a diagnosis of cancer during 2018. Based on the International Classification of Diseases, 9th Revision, Clinical Modification, demography, comorbidity burden, and diagnostic procedures were evaluated, with IHM as our outcome. We evaluated 955 subjects with cancer (23.9% of total hospital admissions), 42.9% were males, and the mean age was 76.4 ± 11.4 years. Metastatic cancer was diagnosed in 18.2%. The deceased group had a higher modified Elixhauser Index (17.6 ± 7.7 vs. 14 ± 7.3, p < 0.001), prevalence of cachexia (17.9% vs. 7.2%, p < 0.001), and presence of metastasis (27.8% vs. 16.3%, p = 0.001) than survivors. Females had a higher age (77.4 ± 11.4 vs. 75.5 ± 11.4, p = 0.013), and lower comorbidity (10.2 ± 5.9 vs. 12.0 ± 5.6, p < 0.001) than males. IHM was not significantly different among sex groups, but it was independently associated with cachexia and metastasis only in women. Comorbidities are highly prevalent in patients with cancer admitted to the internal medicine setting and are associated with an increased risk of all-cause mortality, especially in female elderly patients with advanced disease.

Competing-risk analysis of coronavirus disease 2019 in-hospital mortality in a Northern Italian centre from SMAtteo COvid19 REgistry (SMACORE)

An accurate prediction of the clinical outcomes of European patients requiring hospitalisation for Coronavirus Disease 2019 (COVID-19) is lacking. The aim of the study is to identify predictors of in-hospital mortality and discharge in a cohort of Lombardy patients with COVID-19. All consecutive hospitalised patients from February 21st to March 30th, 2020, with confirmed COVID-19 from the IRCCS Policlinico San Matteo, Pavia, Lombardy, Italy, were included. In-hospital mortality and discharge were evaluated by competing risk analysis. The Fine and Gray model was fitted in order to estimate the effect of covariates on the cumulative incidence functions (CIFs) for in-hospital mortality and discharge. 426 adult patients [median age 68 (IQR 56 to 77 years)] were admitted with confirmed COVID-19 over a 5-week period; 292 (69%) were male. By 21 April 2020, 141 (33%) of these patients had died, 239 (56%) patients had been discharged and 46 (11%) were still hospitalised. Among these 46 patients, updated as of 30 May, 2020, 5 (10.9%) had died, 8 (17.4%) were still in ICU, 12 (26.1%) were transferred to lower intensity care units and 21 (45.7%) were discharged. Regression on the CIFs for in-hospital mortality showed that older age, male sex, number of comorbidities and hospital admission after March 4th were independent risk factors associated with in-hospital mortality. Older age, male sex and number of comorbidities definitively predicted in-hospital mortality in hospitalised patients with 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.