Use of all cause mortality to quantify the consequences of covid-19 in Nembro, Lombardy: descriptive study

Piccininni, Marco; Rohmann, Jessica L.; Foresti, Luca; Lurani, Caterina; Kurth, Tobias

doi:10.1136/bmj.m1835

Cited by 64 publications

(71 citation statements)

References 12 publications

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“…The clinical characteristics and clinical course of COVID-19 patients hospitalised in Lyon were similar to those reported in California [6], New York City [9], and Italy [5,7,8], which supports the generalisability of the proposed prediction tool. As reported in the analysis published byWynants et al [10], the most frequently reported diagnostic and prognostic predictors of covid-19 are age, body temperature, lymphocyte count and lung imaging characteristics.…”

Section: Discussionsupporting

confidence: 76%

“…The most frequently reported diagnostic and prognostic predictors of COVID-19 are age, body temperature, lymphocyte count, and lung imaging characteristics. Flu-like symptoms and neutrophil counts are frequently predictive in diagnostic models, while co-morbidities, sex, C-reactive protein and creatinine are frequent prognostic factors [4][5][6][7][8][9][10]. With more than 90% of deaths occurring in patients over the age of 60 years, and a predominance of males, French data are consistent with that reported worldwide [1, [4][5][6][7][8][9].…”

Section: Introductionsupporting

confidence: 66%

“…Flu-like symptoms and neutrophil counts are frequently predictive in diagnostic models, while co-morbidities, sex, C-reactive protein and creatinine are frequent prognostic factors [4][5][6][7][8][9][10]. With more than 90% of deaths occurring in patients over the age of 60 years, and a predominance of males, French data are consistent with that reported worldwide [1, [4][5][6][7][8][9]. In Italy [11], a higher case-fatality rate (7.2%) compared to China (2.3%) was attributed to a higher proportion of elderly people [12], obviously if the methods for classifying and reporting deaths due to COVID-19 disease were the same.…”

Section: Introductionmentioning

confidence: 99%

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Predicting an Unfavourable Course in Patients Hospitalised for COVID-19: The PREDICT-COVID Study

Ducher

Elias

Florens

et al. 2020

Preprint

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Background. Clinical decision tools that have been proposed to predict the clinical course of patients admitted to hospital with COVID-19 are poorly presented and are at high risk of selection bias. The aim of the study was to propose a prediction clinical tool to predict an unfavourable outcome at the admission of a SARS-CoV2 infected patient that was carefully developed using a large learning database and that was developed from models derived from artificial intelligence.Methods. The PREDICT-COVID study is a post hoc analysis of the Noso-Cor study, a multicenter prospective, observational study. All patients infected by SARS-CoV2 hospitalized in one of the 11 Lyon-University hospitals since 8-March-2020 have been included. The PREDICT-COVID database was split in two separate datasets: the learning dataset (80%) was used for the development of the model and the validation dataset (20%) for internal validation. The primary composite outcome was the need for mechanical ventilation or admission into an intensive care unit, or death within 21 days of admission.Results. Data from 823 patients were analysed: age 70.6±16·9 years; body mass index 26.7±5·4 kg/m2 and median number of comorbidities was 2. Out of the 44 recorded variables, 11 that were the most linked to the primary outcome criteria were retained to develop the optimised risk prediction tool. At admission the 5 most informative predictors were, in descending order: C-Reactive Protein, neutrophil-to-lymphocyte ratio, aspartate transaminase, shortness of breath, and prothrombin time. The ten-fold cross validation of the optimised model had an area under the ROC curve of 0.76±0.06. The performance of the developed Bayesian model to predict the primary outcome of the validation dataset had a mean area under the ROC curve of 0.78, sensitivity of 60%, and specificity of 77%.Conclusions. The proposed optimised prediction tool that uses 11 routinely determined variables to predict an unfavourable course at admission for COVID-19 had satisfactory performance. For an external validation, the PREDICT-COVID prediction tool is available online at: https://www.hed.cc/?a=covid&n=NETCRIT21J.netaTrial registration: The Noso-Cor study was registered on ClinicalTrials (NCT04290780). The present analysis was registered on ClinicalTrials (NCT04412031) the June 2, 2020.

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Section: Discussionsupporting

confidence: 76%

Section: Introductionsupporting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting an Unfavourable Course in Patients Hospitalised for COVID-19: The PREDICT-COVID Study

Ducher

Elias

Florens

et al. 2020

Preprint

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“…As above reported, these values could be underestimated for reasons depending on several factors like a stringent testing policy limited to severely affected symptomatic individuals, that excluded from testing the bulk of asymptomatic ones, shortage of testing materials in the peak of the emergency, limited access to overcrowded hospital facilities, to name just a few. Noteworthy, a higher overall mortality rate than previous years has been observed in Nembro, a little town of Lombardia region, indicative of both direct and indirect disease burden [4].…”

Section: Discussionmentioning

confidence: 86%

“…hospitals and intensive care units were overcrowded and many people may have died in their homes without testing. A recent observational study conducted on a small community in Nembro, a little town located in Lombardia, one of the most affected areas of northern Italy, reported an all-cause mortality between January and April 2020, several times higher than that recorded in the previous eight years in the same time frame [4]. Overall, Covid-19 deaths were mostly observed in males and older patients with preexisting comorbidities [5,6].…”

Section: Read Full Licensementioning

confidence: 96%

Correlation of the two most frequent HLA haplotypes in the Italian population to the differential regional incidence of Covid-19

Pisanti

Deelen

Gallina

et al. 2020

Preprint

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Background: Understanding how HLA polymorphisms may affect both susceptibility, course and severity of Covid-19 infection could help both at the clinical level to identify individuals at higher risk from the disease and at the epidemiological one to explain the differences in the epidemic trend among countries or even within a specific country. Covid-19 disease in Italy showed a peculiar geographical distribution from the northern most affected regions to the southern ones only slightly touched. Methods: In this study we analysed the regional frequencies for the most common Italian haplotypes from the Italian Bone Marrow Donor Registry (HLA-A, -B, -C and -DRB1 at four-digit level). Then we performed Pearson correlation analyses among regional haplotypes estimated frequency in the population and Covid-19 incidence and mortality.Results: In this study we found that the two most frequent HLA haplotypes in the Italian population, HLA-A*:01:01g-B*08:01g-C*07:01g-DRB1*03:01g and HLA-A*02.01g-B*18.01g-C*07.01g-DRB1*11.04g, had a regional distribution overlapping that of Covid-19 and showed respectively a positive (suggestive of susceptibility) and negative (suggestive of protection) significant correlation with both Covid-19 incidence and mortality. Conclusions: Based on these results, in order to define such HLA polymorphisms as a factor effectively associated to the disease susceptibility, the creation of national networks that can collect patients’ samples from all regions for HLA typing should be highly encouraged.

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Risk factors for excess deaths during lockdown among older users of secondary care mental health services without confirmed COVID‐19: A retrospective cohort study

Chen

Jones

Underwood

et al. 2021

Int J Geriat Psychiatry

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Objective To investigate factors contributing to excess deaths of older patients during the initial 2020 lockdown beyond those attributable to confirmed COVID‐19. Methods Retrospective cohort study comparing patients treated between 23 March 2020 and 14 June 2020, deemed exposed to the pandemic/lockdown, to patients treated between 18 December 2019 and 10 March 2020, deemed to be unexposed. Data came from electronic clinical records from secondary care mental health services in Cambridgeshire and Peterborough NHS Foundation Trust (CPFT), UK (catchment area population ∼0.86 million). Eligible patients were aged 65 years or over at baseline with at least 14 days' follow‐up, excluding patients diagnosed with confirmed or suspected SARS‐CoV‐2 infection. The primary outcome was all‐cause mortality. Findings In the two cohorts, 3,073 subjects were exposed to lockdown and 4,372 subjects were unexposed; the cohorts were followed up for an average of 74 and 78 days, respectively. After controlling for confounding by sociodemographic factors, smoking status, mental comorbidities, and physical comorbidities, patients with dementia suffered an additional 53% risk of death (HR = 1.53, 95% CI = 1.02–2.31), and patients with severe mental illness suffered an additional 123% risk of death (HR = 2.23, 95% CI = 1.42–3.49). No significant additional mortality risks were identified from physical comorbidities, potentially due to low statistical power in that respect. Conclusion During lockdown people with dementia or severe mental illness had a higher risk of death without confirmed COVID‐19. These data could inform future health service responses and policymaking to help prevent avoidable excess death during future outbreaks of this or a similar infectious disease.

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Use of all cause mortality to quantify the consequences of covid-19 in Nembro, Lombardy: descriptive study

Abstract: OBJECTIVE To quantify the impact of coronavirus disease 2019 (covid-19) on all cause mortality in Nembro, an Italian city severely affected by the covid-19 pandemic. DESIGNDescriptive study.

Cited by 64 publications

References 12 publications

Predicting an Unfavourable Course in Patients Hospitalised for COVID-19: The PREDICT-COVID Study

Predicting an Unfavourable Course in Patients Hospitalised for COVID-19: The PREDICT-COVID Study

Correlation of the two most frequent HLA haplotypes in the Italian population to the differential regional incidence of Covid-19

Risk factors for excess deaths during lockdown among older users of secondary care mental health services without confirmed COVID‐19: A retrospective cohort study

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