To address the coronavirus (Covid-19) pandemic,¹ strict social containment measures have been adopted worldwide, and health care systems have been reorganized to cope with the enormous increase in the numbers of acutely ill patients.^2,3 During this same period, some changes in the pattern of hospital admissions for other conditions have been noted. The aim of the present analysis is to investigate the rate of hospital admissions for acute coronary syndrome (ACS) during the early days of the Covid-19 outbreak.

Acute coronary syndromes (ACS) represent a difficult challenge for physicians. Risk scores have become the cornerstone in clinical and interventional decision making. PubMed was systematically searched for ACS risk score studies. They were divided into ACS studies (evaluating Unstable Angina; UA, Non ST Segment Elevation Myocardial Infarction; NSTEMI, and ST Segment Elevation Myocardial Infarction; STEMI), UA/NSTEMI studies or STEMI studies. The c-statistics of validation studies were pooled when appropriate with random-effect methods. 7 derivation studies with 25,525 ACS patients and 15 validation studies including 257,654 people were formally appraised. Pooled analysis of GRACE scores, both at short (0.82; 0.80-0.89 I.C 95%) and long term follow up (0.84; 0.82-0.87; I.C 95%) showed the best performance, with similar results to Simple Risk Index (SRI) derivation cohorts at short term. For NSTEMI/UA, 18 derivation studies with 56,560 patients and 18 validation cohorts with 56,673 patients were included. Pooled analysis of validations studies showed c-statistics of 0.54 (95% CI = 0.52-0.57) and 0.67 (95% CI = 0.62-0.71) for short and long term TIMI validation studies, and 0.83 (95% CI = 0.79-9.87) and 0.80 (95% CI = 0.74-0.89) for short and long term GRACE studies. For STEMI, 15 studies with 134,557 patients with derivation scores, and 17 validation studies with 187,619 patients showed a pooled c-statistic of 0.77 (95% CI = 0.71-0.83) and 0.77 (95% CI = 0.72-0.85) for TIMI at short and long term, and a pooled c-statistic of 0.82 (95% CI = 0.81-0.83) and 0.81 (95% CI = 0.80-0.82) for GRACE at short and long terms respectively. TIMI and GRACE are the risk scores that up until now have been most extensively investigated, with GRACE performing better. There are other potentially useful ACS risk scores available however these have not undergone rigorous validation. This study suggests that these other scores may be potentially useful and should be further researched.

Transcatheter ablation of atrial fibrillation (AF) has undergone important development, with acceptable midterm results in terms of the safety and recurrence. A meta-analysis was performed to identify the periprocedural complications, midterm success rates and predictors of recurrence after AF ablation. 4357 patients with paroxysmal AF, 1083 with persistent AF and 1777 with long standing AF were included. The pooled analysis showed that there was an in-hospital complication rate of tamponade requiring drainage of 0.99% (0.44-1.54; CI 99%), stroke with neurological persistent impairment of 0.22% (0.04-0.47; CI 99%), and stroke without of 0.36% (0.03-0.70; CI 99%) After a follow up of 22 (13-28) months and 1.23 (1.19-1.5; CI 99%) procedures per patient, the AF recurrence rate was 31.20% (24.87-34.81; CI 99%). The persistent AF patients exhibited a greater risk of recurrence after the first ablation (OR 1.78 [1.14, 2.77] CI 99%), but a trend towards non significance was present in the patients with more than one procedure (OR 1.69 [0.95, 3.00] CI 99%). The most powerful predictors of an AF ablation failure in the overall population were a recurrence within 30-days (OR 4.30; 2.00-10.80), valvular AF (OR 5.20; 2.22-9.50) and a left atrium diameter of more than 50mm (OR 5.10 2.00-12.90; all CI 95%). Persistent AF remains burdened from higher recurrence rates, however not so following redo-procedures. Three predictors, valvular AF, a left atrium diameter longer than 50mm and recurrence within 30 days, could be appraised to drive selection of patients and therapeutic strategy.

Clinical medicine requires the integration of various forms of patient data including demographics, symptom characteristics, electrocardiogram findings, laboratory values, biomarker levels, and imaging studies. Decision-making on the optimal management should be based on a high probability that the envisaged treatment is appropriate, provides benefit, and bears no or little potential harm. To that end, personalized risk–benefit considerations should guide the management of individual patients to achieve optimal results. These basic clinical tasks have become more and more challenging with the massively growing data now available; artificial intelligence and machine learning (AI/ML) can provide assistance for clinicians by obtaining and comprehensively preparing the history of patients, analysing face and voice and other clinical features, by integrating laboratory results, biomarkers, and imaging. Furthermore, AI/ML can provide a comprehensive risk assessment as a basis of optimal acute and chronic care. The clinical usefulness of AI/ML algorithms should be carefully assessed, validated with confirmation datasets before clinical use, and repeatedly re-evaluated as patient phenotypes change. This review provides an overview of the current data revolution that has changed and will continue to change the face of clinical medicine radically, if properly used, to the benefit of physicians and patients alike.

The digital transformation of manufacturing (a phenomenon also known as “Industry 4.0” or “Smart Manufacturing”) is finding a growing interest both at practitioner and academic levels, but is still in its infancy and needs deeper investigation. Even though current and potential advantages of digital manufacturing are remarkable, in terms of improved efficiency, sustainability, customization, and flexibility, only a limited number of companies has already developed ad hoc strategies necessary to achieve a superior performance. Through a systematic review, this study aims at assessing the current state of the art of the academic literature regarding the paradigm shift occurring in the manufacturing settings, in order to provide definitions as well as point out recurring patterns and gaps to be addressed by future research. For the literature search, the most representative keywords, strict criteria, and classification schemes based on authoritative reference studies were used. The final sample of 156 primary publications was analyzed through a systematic coding process to identify theoretical and methodological approaches, together with other significant elements. This analysis allowed a mapping of the literature based on clusters of critical themes to synthesize the developments of different research streams and provide the most representative picture of its current state. Research areas, insights, and gaps resulting from this analysis contributed to create a schematic research agenda, which clearly indicates the space for future evolutions of the state of knowledge in this field.