Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 29
Filter
2.
SN Compr Clin Med ; 2(8): 1053-1056, 2020.
Article in English | MEDLINE | ID: covidwho-1706546

ABSTRACT

During novel coronavirus disease (COVID-19) pandemic, major focus of health service is on mitigating the spread of infection and treating the acute severe respiratory syndrome of affected patients. However, from available initial data, it has been shown that cardiovascular and metabolic diseases are responsible for a worse clinical outcome of COVID-19 patients and, on the other hand, myocardial damage might occur as a consequence of infection. Therefore, we propose not to forget the heart during pandemic and to focus on cardiac care in at least three phases: prevention, acute phase, and rehabilitation. We report rationale, scientific evidence, and clinical model for the proposed three-phase program.

3.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-317607

ABSTRACT

Background: The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters, potentially available at home, to help identifying patients with COVID-19 who are at higher risk of death. Methods: The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020 to November 5, 2020. Afterwards, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020 to February 5 2021. The primary outcome was in-hospital mortality.The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of 5-fold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 hours after the baseline measurement was plotted against its baseline value. Results: Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the 5-fold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n=1463) in which the mortality rate was 22.6 %. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the mortality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 hours after admission (adjusted R-squared= 0.48). Conclusions: We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients at home, in the Emergency Department, or during hospitalization.

4.
Comput Methods Programs Biomed ; 217: 106655, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1654240

ABSTRACT

BACKGROUND: The COVID-19 pandemic affected healthcare systems worldwide. Predictive models developed by Artificial Intelligence (AI) and based on timely, centralized and standardized real world patient data could improve management of COVID-19 to achieve better clinical outcomes. The objectives of this manuscript are to describe the structure and technologies used to construct a COVID-19 Data Mart architecture and to present how a large hospital has tackled the challenge of supporting daily management of COVID-19 pandemic emergency, by creating a strong retrospective knowledge base, a real time environment and integrated information dashboard for daily practice and early identification of critical condition at patient level. This framework is also used as an informative, continuously enriched data lake, which is a base for several on-going predictive studies. METHODS: The information technology framework for clinical practice and research was described. It was developed using SAS Institute software analytics tool and SAS® Vyia® environment and Open-Source environment R ® and Python ® for fast prototyping and modeling. The included variables and the source extraction procedures were presented. RESULTS: The Data Mart covers a retrospective cohort of 5528 patients with SARS-CoV-2 infection. People who died were older, had more comorbidities, reported more frequently dyspnea at onset, had higher d-dimer, C-reactive protein and urea nitrogen. The dashboard was developed to support the management of COVID-19 patients at three levels: hospital, single ward and individual care level. INTERPRETATION: The COVID-19 Data Mart based on integration of a large collection of clinical data and an AI-based integrated framework has been developed, based on a set of automated procedures for data mining and retrieval, transformation and integration, and has been embedded in the clinical practice to help managing daily care. Benefits from the availability of a Data Mart include the opportunity to build predictive models with a machine learning approach to identify undescribed clinical phenotypes and to foster hospital networks. A real-time updated dashboard built from the Data Mart may represent a valid tool for a better knowledge of epidemiological and clinical features of COVID-19, especially when multiple waves are observed, as well as for epidemic and pandemic events of the same nature (e. g. with critical clinical conditions leading to severe pulmonary inflammation). Therefore, we believe the approach presented in this paper may find several applications in comparable situations even at region or state levels. Finally, models predicting the course of future waves or new pandemics could largely benefit from network of DataMarts.


Subject(s)
COVID-19 , Artificial Intelligence , COVID-19/epidemiology , Clinical Decision-Making , Humans , Pandemics , Retrospective Studies , SARS-CoV-2
5.
European heart journal supplements : journal of the European Society of Cardiology ; 23(Suppl G), 2021.
Article in English | EuropePMC | ID: covidwho-1601811

ABSTRACT

Aims Due to its bidimensional nature, angiography is not always sufficient to accurately define coronary lesions, in particular when they are ambiguous or indeterminate. Intracoronary imaging, such as intravascular ultrasound or optical coherence tomography (OCT), is often useful in these cases to better characterize the ambiguous angiographic images, to identify the culprit lesion during acute coronary syndrome (ACS) and to guide percutaneous coronary intervention (PCI). Methods and results We report a case of a 61-year-old male with multiple cardiovascular risk factors and a previous ST-segment elevation myocardial infarction treated by PCI of the right coronary artery (RCA) about 7 years before, wo was admitted to our emergency department after acute onset chest pain. At the time of admission, his ECG was normal and cardiac troponin was below the upper reference limit of normality with positive molecular SARS-CoV-2 diagnostic test. Echocardiogram disclosed a mild left ventricular dysfunction with inferior wall hypokinesia. Coronary angiography showed a moderate in-stent restenosis at mid RCA and a hazy, undetermined image at the proximal edge of the previously implanted stent. Left coronary artery angiography showed only diffuse atherosclerotic disease without significant stenoses and a myocardial bridge at the mid tract of left anterior descending artery. OCT pullback of RCA to better characterize the undetermined lesions shown by angiography. OCT revealed significant neointima hyperplasia and a focal area of neoatherosclerosis with unstable features (fissure/microthrombi) at mid RCA. Severe stent strut malapposition embedded neointimal hyperplasia was observed at the proximal stent edge, resulting in ‘dual’ lumen appearance. The two lesions were treated with a single 3.5/48 mm everolimus-eluting stent (stent-in-stent), which was post-dilated with a 3.5/20 mm non-compliant balloon (18 atm) in the mid-to-distal segments, and 4.5/15 mm (16 atm) and 5.0/8 mm (14 atm) semi-compliant balloons in the proximal stent segment. Post-PCI OCT imaging confirmed good stent expansion and apposition. Our case demonstrates the utility of OCT in clarifying the aetiology of ambiguous angiographic lesions and as a guide for PCI. Indeed, the ‘hazy’ appearance on the angiograms corresponded to the major stent malapposition covered by neointima disclosed by OCT as a ‘dual-lumen’. Of note, OCT allowed to confirm the correct guidewire position in the ‘true’ lumen preventing a crush of the previously implanted stent. OCT was also useful as a diagnostic modality for the identification and characterization of the mechanism underlying the ACS (neoatherosclerosis instability). Conclusions Due to its unprecedented spatial resolution, OCT enables an ‘optical biopsy’ of the coronary artery wall and intrastent tissue. Therefore, OCT imaging should be considered when lesions are ambiguous or indetermined by coronary angiography to guide the diagnosis and treatments of ACS patients. OCT imaging is also useful to guide stenting and to optimize PCI result, and its impact on clinical outcome is under investigation in large randomized clinical trials.

6.
Sci Rep ; 11(1): 21136, 2021 10 27.
Article in English | MEDLINE | ID: covidwho-1493228

ABSTRACT

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home.


Subject(s)
COVID-19/mortality , Machine Learning , Pandemics , SARS-CoV-2 , Aged , Aged, 80 and over , Blood Cell Count , Blood Chemical Analysis , COVID-19/blood , Cohort Studies , Female , Hospital Mortality , Humans , Male , Middle Aged , Models, Statistical , Multivariate Analysis , Oxygen/blood , Pandemics/statistics & numerical data , ROC Curve , Risk Factors , Rome/epidemiology
7.
J Clin Med ; 10(21)2021 Oct 26.
Article in English | MEDLINE | ID: covidwho-1488629

ABSTRACT

BACKGROUND: A prothrombotic state, attributable to excessive inflammation, cytokine storm, hypoxia, and immobilization, is a feature of SARS-CoV-2 infection. Up to 30% of patients with severe COVID-19 remain at high risk of thromboembolic events despite anticoagulant administration, with adverse impact on in-hospital prognosis. METHODS: We retrospectively studied 4742 patients with acute infectious respiratory disease (AIRD); 2579 were diagnosed to have COVID-19 and treated with heparin, whereas 2163 had other causes of AIRD. We compared the incidence and predictors of total, arterial, and venous thrombosis, both in the whole population and in a propensity score-matched subpopulation of 3036 patients (1518 in each group). RESULTS: 271 thrombotic events occurred in the whole population: 121 (4.7%) in the COVID-19 group and 150 (6.9%) in the no-COVID-19 group (p < 0.001). No differences in the incidence of total (p = 0.11), arterial (p = 0.26), and venous (p = 0.38) thrombosis were found between the two groups after adjustment for confounding clinical variables and in the propensity score-matched subpopulation. Likewise, there were no significant differences in bleeding rates between the two groups. Clinical predictors of arterial thrombosis included age (p = 0.006), diabetes mellitus (p = 0.034), peripheral artery disease (p < 0.001), and previous stroke (p < 0.001), whereas history of solid cancer (p < 0.001) and previous deep vein thrombosis (p = 0.007) were associated with higher incidence of venous thrombosis. CONCLUSIONS: Hospitalized patients with COVID-19 treated with heparin do not seem to show significant differences in the cumulative incidence of thromboembolic events as well as in the incidence of arterial and venous thrombosis separately, compared with AIRD patients with different etiological diagnosis.

8.
Eur Heart J ; 42(11): 1053-1056, 2021 03 14.
Article in English | MEDLINE | ID: covidwho-1472268
11.
Minerva Cardiol Angiol ; 69(4): 377-388, 2021 08.
Article in English | MEDLINE | ID: covidwho-1431235

ABSTRACT

From first cases reported on December 31, 2019, in Wuhan, Hubei-China, SARS-CoV2 has spread worldwide and finally the World Health Organization declared the pandemic status. We summarize what makes SARS-CoV2 different from previous highly pathogenic coronaviruses and why it is so contagious, with focus on its clinical presentation and diagnosis, which is mandatory to start the appropriate management and reduce the transmission. As far as infection pathophysiology is still not completely clarified, this review focuses also on the cardiovascular (CV) implication of COVID-19 and the capability of this virus to cause direct myocardial injury, myocarditis and other CV manifestations. Furthermore, we highlight the relationship between the virus, enzyme ACE2 and ACE inhibitors. Clinical management involves the intensive care approach with intubation and mechanical ventilation in the most serious cases and drug therapy with several apparently promising old and new molecules. Aim of this review is then to summarize what is actually known about the SARS-CoV2 and its cardiovascular implications.


Subject(s)
COVID-19 , Cardiovascular System , Humans , Pandemics , RNA, Viral , SARS-CoV-2
12.
Europace ; 23(1): 123-129, 2021 01 27.
Article in English | MEDLINE | ID: covidwho-1387869

ABSTRACT

AIMS: The main severe complications of SARS-CoV-2 infection are pneumonia and respiratory distress syndrome. Recent studies, however, reported that cardiac injury, as assessed by troponin levels, is associated with a worse outcome in these patients. No study hitherto assessed whether the simple standard electrocardiogram (ECG) may be helpful for risk stratification in these patients. METHODS AND RESULTS: We studied 324 consecutive patients admitted to our Emergency Department with a confirmed diagnosis of SARS-CoV-2 infection. Standard 12-lead ECG recorded on admission was assessed for cardiac rhythm and rate, atrioventricular and intraventricular conduction, abnormal Q/QS wave, ST segment and T wave changes, corrected QT interval, and tachyarrhythmias. At a mean follow-up of 31 ± 11 days, 44 deaths occurred (13.6%). Most ECG variables were significantly associated with mortality, including atrial fibrillation (P = 0.002), increasing heart rate (P = 0.002), presence of left bundle branch block (LBBB; P < 0.001), QRS duration (P <0 .001), a QRS duration of ≥110 ms (P < 0.001), ST segment depression (P < 0.001), abnormal Q/QS wave (P = 0.034), premature ventricular complexes (PVCs; P = 0.051), and presence of any ECG abnormality [hazard ratio (HR) 4.58; 95% confidence interval (CI) 2.40-8.76; P < 0.001]. At multivariable analysis, QRS duration (P = 0.002), QRS duration ≥110 ms (P = 0.03), LBBB (P = 0.014) and presence of any ECG abnormality (P = 0.04) maintained a significant independent association with mortality. CONCLUSION: Our data show that standard ECG can be helpful for an initial risk stratification of patients admitted for SARS-CoV-2 infectious disease.


Subject(s)
COVID-19/complications , Electrocardiography , Heart Conduction System/physiopathology , Heart Diseases/diagnosis , Heart Rate , Action Potentials , Aged , Aged, 80 and over , COVID-19/diagnosis , COVID-19/mortality , Female , Heart Diseases/etiology , Heart Diseases/mortality , Heart Diseases/physiopathology , Hospital Mortality , Hospitalization , Humans , Male , Middle Aged , Predictive Value of Tests , Prognosis , Risk Assessment , Risk Factors , Time Factors
14.
Int J Mol Sci ; 22(12)2021 Jun 21.
Article in English | MEDLINE | ID: covidwho-1282515

ABSTRACT

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been associated with excess mortality worldwide. The cardiovascular system is the second most common target of SARS-CoV-2, which leads to severe complications, including acute myocardial injury, myocarditis, arrhythmias, and venous thromboembolism, as well as other major thrombotic events because of direct endothelial injury and an excessive systemic inflammatory response. This review focuses on the similarities and the differences of inflammatory pathways involved in COVID-19 and atherosclerosis. Anti-inflammatory agents and immunomodulators have recently been assessed, which may constitute rational treatments for the reduction of cardiovascular events in both COVID-19 and atherosclerotic heart disease.


Subject(s)
Atherosclerosis/pathology , COVID-19/pathology , Adrenal Cortex Hormones/therapeutic use , Anti-Inflammatory Agents/therapeutic use , Atherosclerosis/complications , Atherosclerosis/drug therapy , Atherosclerosis/prevention & control , COVID-19/complications , COVID-19/drug therapy , COVID-19/virology , Chemokines/metabolism , Cytokine Release Syndrome/etiology , Cytokines/metabolism , Humans , Prognosis , SARS-CoV-2/isolation & purification , SARS-CoV-2/metabolism
15.
J Clin Med ; 10(10)2021 May 14.
Article in English | MEDLINE | ID: covidwho-1234751

ABSTRACT

BACKGROUND: The COVID-19 pandemic has had a deep impact on periodic outpatient evaluations. The aim of this study was to evaluate the impact of low brain natriuretic peptide (BNP) values in predicting adverse events in heart failure (HF) patients in order to evaluate implications for safe delay of outpatient visits. METHODS: This was a retrospective study. One-thousand patients (mean age: 72 ± 10 years, 561 women) with HF and BNP values <250 pg/mL at discharge were included. A 6-month follow-up was performed. The primary endpoint was a combination of deaths and readmissions for HF within 6-month after discharge. RESULTS: At 6-month follow-up, 104 events (10.4%) were recorded (65 HF readmissions and 39 all-cause deaths). Univariate Cox analysis identified as significant predictors of outcome were age (p < 0.001, hazard ratio [HR] = 1.044), creatinine (p = 0.001, HR = 1.411), and BNP (p < 0.001, HR = 1.010). Multivariate Cox regression confirmed that BNP (p < 0.001, HR = 1.009), creatinine (p = 0.016, HR = 1.247), and age (p = 0.013, HR = 1.027) were independent predictors of events in HF patients with BNP values <250 pg/mL at discharge. Patients with BNP values >100 pg/mL and creatinine >1.0 mg/dL showed increased events rates (from 4.3% to 19.0%) as compared to those with lower values (p < 0.000, HR = 4.014). CONCLUSIONS: Low pre-discharge BNP levels were associated with low rates of cardiovascular events in HF patients, independently of the frequency of follow-up.

16.
Eur Heart J ; 42(19): 1813-1817, 2021 05 14.
Article in English | MEDLINE | ID: covidwho-1228489
17.
J Cardiovasc Med (Hagerstown) ; 22(9): 706-710, 2021 09 01.
Article in English | MEDLINE | ID: covidwho-1197501

ABSTRACT

AIM: To summarize our experience on the implementation of a telemedicine service dedicated to adult congenital heart disease (ACHD) patients during the lockdown for the first wave of Coronavirus disease 2019 (COVID-19). METHODS: This is a prospective study enrolling all ACHD patients who answered a questionnaire dedicated telematic cardiovascular examination. RESULTS: A total of 289 patients were enrolled, 133 (47%) were male, 25 (9%) were affected by a genetic syndrome. The median age was 38 (29-51) years, whereas the median time interval between the last visit and the telematic follow-up was 9.5 (7.5-11.5) months. Overall, 35 patients (12%) reported a worsening of fatigue in daily life activity, 17 (6%) experienced chest pain, 42 (15%) had presyncope and 2 (1%) syncope; in addition, 28 patients (10%) presented peripheral edema and 14 (5%) were orthopneic. A total of 116 (40%) patients reported palpitations and 12 had at least one episode of atrial fibrillation and underwent successful electrical (8) or pharmacological (4) cardioversion. One patient was admitted to the emergency department for uncontrolled arterial hypertension, five for chest pain, and one for heart failure. Two patients presented fever but both had negative COVID-19 nasal swab. CONCLUSION: During the COVID-19 pandemic, the use of telemedicine dramatically increased and here we report a positive experience in ACHD patients. The postpandemic role of telemedicine will depend on permanent regulatory solutions and this early study might encourage a more systematic telematic approach for ACHD patients.


Subject(s)
COVID-19 , Heart Defects, Congenital , Infection Control , Patient Care Management , Patient Preference/statistics & numerical data , Telemedicine , Adult , COVID-19/epidemiology , COVID-19/prevention & control , Female , Heart Defects, Congenital/epidemiology , Heart Defects, Congenital/physiopathology , Heart Defects, Congenital/therapy , Humans , Infection Control/methods , Infection Control/organization & administration , Italy/epidemiology , Male , Outcome and Process Assessment, Health Care , Patient Care Management/methods , Patient Care Management/statistics & numerical data , Prospective Studies , SARS-CoV-2 , Surveys and Questionnaires , Symptom Assessment/methods , Telemedicine/methods , Telemedicine/organization & administration
20.
Future Cardiol ; 17(6): 991-997, 2021 09.
Article in English | MEDLINE | ID: covidwho-983819

ABSTRACT

Amiodarone is a drug commonly used to treat and prevent cardiac arrhythmias, but it is often associated with several adverse effects, the most serious of which is pulmonary toxicity. A 79-year-old man presented with respiratory failure due to interstitial pneumonia during the COVID-19 pandemic. The viral etiology was nevertheless excluded by repeated nasopharyngeal swabs and serological tests and the final diagnosis was amiodarone-induced organizing pneumonia. The clinical and computed tomography findings improved after amiodarone interruption and steroid therapy. Even during a pandemic, differential diagnosis should always be considered and pulmonary toxicity has to be taken into account in any patient taking amiodarone and who has new respiratory symptoms.


Subject(s)
Amiodarone/adverse effects , Anti-Arrhythmia Agents/adverse effects , Lung Diseases, Interstitial/chemically induced , Lung Diseases, Interstitial/diagnosis , Aged , COVID-19/diagnosis , Diagnosis, Differential , Humans , Male , Pandemics , SARS-CoV-2 , Tomography, X-Ray Computed
SELECTION OF CITATIONS
SEARCH DETAIL