COVID-19 presents with a wide spectrum of clinical and radiological manifestations, including pleural effusion. The prevalence and prognostic impact of pleural effusion are still not entirely clear.
...This is a retrospective, single-center study including a population of consecutive patients admitted to the University Hospital of Cisanello (Pisa) from March 2020 to January 2021 with a positive SARS-CoV-2 nasopharyngeal swab and SARS-CoV-2-related pneumonia. The patients were divided into two populations based on the presence (
= 150) or absence (
= 515) of pleural effusion on chest CT scan, excluding patients with pre-existing pleural effusion. We collected laboratory data (hemoglobin, leukocytes, platelets, C-reactive protein, procalcitonin), worst PaO
/FiO
ratio as an index of respiratory gas exchange impairment, the extent of interstitial involvement related to SARS-CoV-2 pneumonia and data on intensity of care, length of stay and outcome (discharge or death).
The prevalence of pleural effusion was 23%. Patients with pleural effusion showed worse gas exchange (
< 0.001), longer average hospital stay (
< 0.001), need for more health care resources (
< 0.001) and higher mortality (
< 0.001) compared to patients without pleural effusion. By multivariate analysis, pleural effusion was found to be an independent negative prognostic factor compared with other variables such as increased C-reactive protein, greater extent of pneumonia and older age. Pleural effusion was present at the first CT scan in most patients (68%).
Pleural effusion associated with SARS-CoV-2 pneumonia is a relatively frequent finding that is confirmed to be a negative prognostic factor. Identifying early prognostic factors in an endemic-prone disease such as COVID-19 is necessary to optimize its clinical management. Further clinical studies aimed at better characterizing pleural effusion in these patients will be appropriate in order to clarify its pathogenetic role.
Readmissions are hospitalizations following a previous hospitalization (called index hospitalization) of the same patient that occurred in the same facility or nursing home. They may be a consequence ...of the progression of the natural history of a disease, but they may also reveal a previous suboptimal stay, or ineffective management of the underlying clinical condition. Preventing avoidable readmissions has the potential to improve both a patient's quality of life, by avoiding exposure to the risks of re-hospitalization, and the financial well-being of health care systems.
We investigated the magnitude of 30 day repeat hospitalizations for the same Major Diagnostic Category (MDC) in the Azienda Ospedaliero Universitaria Pisana (AOUP) over the period from 2018 to 2021. Records were divided into only admissions, index admissions and repeated admission. The length of the stay of all groups was compared using analysis of variance and subsequent multi-comparison tests.
Results showed a reduction in readmissions over the period examined (from 5.36% in 2018 to 4.46% in 2021), likely due to reduced access to care during the COVID-19 pandemic. We also observed that readmissions predominantly affect the male sex, older age groups, and patients with medical Diagnosis Related Groups (DRGs). The length of stay of readmissions was longer than that of index hospitalization (difference of 1.57 days, 95% CI 1.36-1.78 days,
< 0.001). The length of stay of index hospitalization is longer than that of single hospitalization (difference of 0.62 days, 95% CI 0.52-0.72 days,
< 0.001).
A patient who goes for readmission thus has an overall hospitalization duration of almost two and a half times the length of the stay of a patient with single hospitalization, considering both index hospitalization and readmission. This represents a heavy use of hospital resources, about 10,200 more inpatient days than single hospitalizations, corresponding to a 30-bed ward working with an occupancy rate of 95%. Knowledge of readmissions is an important piece of information in health planning and a useful tool for monitoring the quality of models of patient care.
SARS-CoV-2 management in Emergency Department: risk stratification and care setting identification proposal based on first pandemic wave in Pisa University Hospital
Background: COVID-19 patients ...require early treatment and admission to an appropriate care setting, considering possible rapid and unpredictable to Severe Acute Respiratory Syndrome.
Objective: A flow-chart was developed by a multidisciplinary team of Emergency Department (ED) clinicians, intensivists and radiologists aiming to provide tools for disease severity stratification, appropriate ventilation strategy and hospitalization setting identification.
Methods: We conducted a retrospective application of our model on 313 hospitalized patients at Pisa University Hospital including 222 patients admitted to ED for respiratory failure between March and April 2020. Risk stratification score was based on respiratory and chest imaging parameters, while management strategy on comorbidities and age.
Results: Age, comorbidities, clinical respiratory and arterial blood gas parameters, semi-quantitative chest computed tomography score were significant predictors of mortality (p<0,05). Mortality rate was higher in patients treated in intensive care units (26,5%) and undergoing endo-tracheal intubation (32,7%), compared to medical area (21,3%). We verified a good concordance (81,7%) between the proposed model and actual evaluation in ED. Outcomes analysis of subgroups of patients homogeneous for baseline features allowed to verify safety of our model: in non-elderly and/or non-comorbid patients (15% mortality) our scheme overestimates the risk in 30% of cases, but it suggests non-intensive management in patients with reduced functional reserve, elderly and with comorbidities (50% mortality).
Conclusion: Correct management of respiratory failure COVID-19 patients is crucial in this unexpected pandemic. Our flow-chart, despite retrospectively application in small sample, could represents a valid and safe proposal for evaluation in ED.
Tuberculosis (TB) surveillance systems have some pitfalls outside of a National Tuberculosis Program and lack of efficient surveillance hampers accurate epidemiological quantification of TB burden.In ...the present study we assessed the quality of surveillance at the University Hospital in Pisa (UHP), Italy, and TB incidence rates over a ten year period (1999-2008).
Assessment of underreporting was done by record-linkage from two sources: databases of TB diagnoses performed in the UHP and the Italian Infectious Disease Surveillance (IIDS) system. Two different databases were examined: a) TB diagnoses reported in the Hospital Discharge Records (HDR) from three Units of UHP (Respiratory Pathophysiology, Pulmonology and Infectious Diseases Units) (TB database A); b) TB diagnoses reported in HDR of all Units of UHP plus TB positive cases obtained by the Laboratory Register (LR) of UHP (TB database B). For the TB database A, the accuracy of TB diagnosis in HDR was assessed by direct examination of the Clinical Record Forms of the cases. For the TB database B, clinical and population data were described, as well as the trend of incidence and underreporting over 10 yrs.
In the first study 293 patients were found: 80 patients (27%) with a confirmed TB diagnosis were underreported, 39 of them were microbiologically confirmed. Underreporting was related to age (Reported vs Non Reported, mean age: 49.27 ± 20 vs 55 ± 19, p < 0.005 ), diagnosis (smear positive vs negative cases 18.7 vs 81.2%, p = 0.001), microbiological confirmation (49% vs 51%, p < 0.05), X-ray findings (cavitary vs non-cavitary cases: 12.5 vs 87.5%, p = 0.001) but not to nationality.In the second study, 666 patients were found. Mean underreporting rate was 69.4% and decreased over time (68% in 1999, 48% in 2008). Newly diagnosed TB cases were also found to decrease in number whereas immigration rate increased. Underreporting was related to nationality (Immigrants vs Italians: 18% vs 68%, p < 0.001), diagnosis (microbiological confirmation: 25% vs 75%, p < 0.01), kind of hospital regimen (hospitalized patients vs Day Hospital: 70% vs 16%, p < 0.001), and position of TB code in the HDR (TB code in first position vs in the following position: 39,5% vs 45% p < 0.001).
TB is underreported in Pisa, particularly in older patients and those without microbiological confirmation. The TB code in first position of HDR seems fairly accurate in confirming TB diagnosis.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•In our experience chest CT had a significantly higher specificity and accuracy in detecting COVID-19 pneumonia than previously reported.•Chest CT and RT-PCR positive rates were 485/773 (62.7 %) and ...462/773 (59.7 %), respectively.•CT sensitivity and specificity for COVID 19 with RT-PCR as reference were 90.7 % and 78.8 % respectively.•CT PPV, NPV and accuracy were 86.4 %, 85.1 % and 85.9 % respectively.
The goal of this study was to assess chest computed tomography (CT) diagnostic accuracy in clinical practice using RT-PCR as standard of reference.
From March 4th to April 9th 2020, during the peak of the Italian COVID-19 epidemic, we enrolled a series of 773 patients that performed both non-contrast chest CT and RT-PCR with a time interval no longer than a week due to suspected SARS-CoV-2 infection. The diagnostic performance of CT was evaluated according to sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy, considering RT-PCR as the reference standard. An analysis on the patients with discrepant CT scan and RT-PCR result and on the patient with both negative tests was performed.
RT-PCR testing showed an overall positive rate of 59.8 %. CT sensitivity, specificity, PPV, NPV, and accuracy for SARS-CoV-2 infection were 90.7 % 95 % IC, 87.7%–93.2%, 78.8 % 95 % IC, 73.8−83.2%, 86.4 % 95 % IC, 76.1 %–88.9 %, 85.1 % 95 % IC, 81.0 %–88.4 and 85.9 % 95 % IC 83.2−88.3%, respectively. Twenty-five/66 (37.6 %) patients with positive CT and negative RT-PCR results and 12/245 (4.9 %) patients with both negative tests were nevertheless judged as positive cases by the clinicians based on clinical and epidemiological criteria and consequently treated.
In our experience, in a context of high pre-test probability, CT scan shows good sensitivity and a consistently higher specificity for the diagnosis of COVID-19 pneumonia than what reported by previous studies, especially when clinical and epidemiological features are taken into account.
ITALUNG is contributing to the European evaluation of low-dose CT (LDCT) screening for lung cancer (LC).
Eligible subjects aged 55-69 years, smokers or ex-smokers (at least 20 pack-years in the last ...10 years), were randomised to receive an annual invitation for LDCT screening for 4 years (active group) or to usual care (control group). All participants were followed up for vital status and cause of death (at the end of 2014) and LC incidence (at the end of 2013). Pathological and clinical information was collected from the Tuscan Cancer Registry data.
1613 subjects were randomly assigned to the active group and 1593 to the control group. At the end of the follow-up period 67 LC cases were diagnosed in the active group and 71 in the control group (rate ratio (RR)=0.93; 95% CI 0.67 to 1.30). A greater proportion of stage I LC was observed in the active group (36% vs 11%, p<0.001). Non-significant reductions of 17% (RR=0.83; 95% CI 0.67 to 1.03) for overall mortality and 30% (RR=0.70; 95% CI 0.47 to 1.03) for LC-specific mortality were estimated.
Despite the lack of statistical significance, the ITALUNG trial outcomes suggest that LDCT screening could reduce LC and overall mortality. Moreover, the comparison of the number of LC cases diagnosed in the two groups does not show overdiagnosis after an adequate follow-up period. A pooled analysis of all European screening trials is advocated to assess the benefit-to-harm ratio of LDCT screening and its implementation in public health settings.
Results, NCT02777996.
Benefits and harms of long-term anticoagulant therapy (AT) after acute pulmonary embolism (PE) are poorly known. The aim of this study was to investigate the outcome of patients with PE treated with ...AT for 5 years according to American College of Chest Physicians (ACCP) guidelines.Patients with both unprovoked and secondary PE were consecutively enrolled in a "real life" study. After a 12-month AT, they continued or stopped the treatment according to ACCP guidelines, and were followed-up for 5 years. Outcomes were all-cause mortality, recurrence, and fatal recurrence under AT.Of the original consecutive 585 patients, 471 were included (83 dead, 31 lost during the 1st year). Of these, 361 (76.6%) continued AT. During 5 years, death occurred in 109 (30.2%) patients, with a mortality rate of 8.00 events/100 person-years of follow-up; recurrence in 34 (9.4%), with an incidence rate of 2.58 events/person-years; fatal recurrence in 13 (3.6%), with an incidence rate of 0.95 events/person-years. The case fatality rate for recurrence was 38.2%. In the subgroup of patients with unprovoked PE, the chance of dying was significantly lower (RR 0.35; 95% confidence interval 0.24-0.53) and the tendency to fatal recurrence (not significantly) greater (0.11 events/100 person-years vs 0.07 events/100 person-years) than in the remaining patients. Major bleeding occurred in 5 (1.3%) patients. The case fatality rate for bleeding was 14.3%.During 5-year AT, 30% of patients dies, 10% experiences recurrences, and 5% has fatal recurrences. According to guidelines, most patients need to continue AT; the case fatality rate for bleeding is lower than that for recurrence.
•We made a double blind assessment of ADC ratio in 98 MRI identified lesions.•We correlated ADC ratios with the bioptic results.•ADC ratio (mean) was moderately accurate in assessing the malignity of ...MRI identified lesions.•AUC of ADC ratio mean was 0,72 in Reader 1 and 0,74 in Reader 2.•The best cut-off was ≥0.5 for the Reader 1 and ≥0.6 for the Reader 2.
Our goal was to evaluate the usefulness of apparent diffusion coefficient (ADC) ratios in discriminating true from false positives in multiparametric (mp) prostate MRI in clinical practice.
We retrospectively evaluated 98 prostate lesions in a series of 73 patients who had undergone prostate mpMRI and standard 12-core prostatic biopsy in our institution from 2016 to 2018. Two experienced radiologists performed double blind ADC value quantifications of both MRI-identified lesions and apparently benign contralateral prostatic parenchyma in a circular region of interest (ROI) of ∼10 mm2. The ratios between the mean values of both measurements (i.e., ADC ratio mean) and between the minimum value of the lesion and the maximum value of the benign parenchyma (i.e., ADC ratio min-max) were automatically calculated. The malignancy of all lesions was determined through biopsy according to Gleason score (GS ≥ 6) and localization.
For Reader 1, the area under the ROC curve (AUC) of ADC ratio mean and ADC ratio min-max were 0.72 and 0.67, respectively, whereas for Reader 2 these values were 0.74 and 0.71, respectively. The best cut-off values for ADC ratio means were ≥ 0.5 (Reader 1) and ≥ 0.6 (Reader 2), with a sensitivity of 76.3 % and 84.2 % and a specificity of 51.7 % and 50 %, respectively. Moreover, based on a threshold of 0.6, no clinically significant prostate cancer (csPCa) was missed by Reader 1, while only one went unnoticed by Reader 2.
The ADC ratio is a useful and moderately accurate complementary tool to diagnose prostate cancer in the mp-MRI.
Abstract
Background
Changes in smoking habits and predictors of smoking cessation were examined in the randomized ITALUNG lung cancer screening trial.
Methods
In three centers, eligible smokers or ...ex-smokers (55–69 years, ≥20 pack-years in the last 10 years) were randomized to receive annual invitation for low-dose computed tomography for 4 years or usual care. At invitation, subjects received written information for a free smoking cessation program. Quitting outcome was assessed at year 4.
Results
Among participants who completed baseline assessments and year 4 screening, higher quitting (20.8% vs. 16.7%, p = .029) and lower relapse (6.41% vs. 7.56%, p = .50) rates were observed in the active screening group as compared to the usual-care control group. Corresponding figures in the intention-to-treat analysis were as follows: 16.04% versus 14.64% (p = .059) and 4.88% versus 6.43% (p = .26). Quitting smoking was significantly associated to male gender, lower pack-years, and having pulmonary nodules at baseline. Center-specific analyses showed a threefold statistically significant higher probability to quit associated with participating in the smoking cessation program. A subsample of smokers of the scan group from one center showed higher quitting rates over 12-month follow-up as compared to matched controls from the general population who underwent the same smoking cessation program.
Conclusions
Consistently with previous reports, in the ITALUNG trial, screened subjects showed significantly higher quit rates than controls, and higher quit rates were associated with both the presence of pulmonary nodules and participating in a smoking cessation program. Maximal effect on quitting outcome was observed with the participation in the smoking cessation program.
Implications
Participating in lung cancer screening promotes smoking cessation. An effective “teachable moment” may be achieved when the smoking cessation intervention is structured as integral part of the screening clinical visits and conducted by a dedicated team of health care professionals. Standardized guidelines for smoking cessation interventions in lung cancer screening are needed.
PDF
