Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Relationship between out-of-hospital cardiac arrests and COVID-19 during the first and second pandemic wave. The importance of monitoring COVID-19 incidence

  • Enrico Baldi,

    Roles Conceptualization, Data curation, Writing – original draft

    Affiliations Department of Molecular Medicine, Section of Cardiology, University of Pavia, Pavia, Italy, Cardiac Intensive Care Unit, Arrhythmia and Electrophysiology and Experimental Cardiology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy

  • Roberto Primi,

    Roles Data curation, Formal analysis, Methodology

    Affiliation Division of Cardiology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy

  • Sara Bendotti,

    Roles Data curation, Formal analysis, Methodology

    Affiliation Division of Cardiology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy

  • Alessia Currao,

    Roles Data curation, Formal analysis, Methodology

    Affiliation Division of Cardiology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy

  • Sara Compagnoni,

    Roles Data curation, Writing – original draft

    Affiliations Department of Molecular Medicine, Section of Cardiology, University of Pavia, Pavia, Italy, Division of Cardiology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy

  • Francesca Romana Gentile,

    Roles Data curation, Investigation

    Affiliations Department of Molecular Medicine, Section of Cardiology, University of Pavia, Pavia, Italy, Division of Cardiology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy

  • Giuseppe Maria Sechi,

    Roles Supervision

    Affiliation Agenzia Regionale Emergenza Urgenza, Milan, Italy

  • Claudio Mare,

    Roles Supervision

    Affiliation Agenzia Regionale Emergenza Urgenza, Milan, Italy

  • Alessandra Palo,

    Roles Supervision

    Affiliation AAT Pavia - Agenzia Regionale Emergenza Urgenza (AREU) c/o Fondazione IRCCS Policlinico San Matteo, Pavia, Italy

  • Enrico Contri,

    Roles Data curation

    Affiliation AAT Pavia - Agenzia Regionale Emergenza Urgenza (AREU) c/o Fondazione IRCCS Policlinico San Matteo, Pavia, Italy

  • Vincenza Ronchi,

    Roles Data curation

    Affiliations AAT Pavia - Agenzia Regionale Emergenza Urgenza (AREU) c/o Fondazione IRCCS Policlinico San Matteo, Pavia, Italy, ASST di Pavia, Pavia, Italy

  • Giuseppe Bergamini,

    Roles Supervision

    Affiliation AAT Lodi - Agenzia Regionale Emergenza Urgenza (AREU) c/o ASST di Lodi, Lodi, Italy

  • Francesca Reali,

    Roles Data curation

    Affiliation AAT Lodi - Agenzia Regionale Emergenza Urgenza (AREU) c/o ASST di Lodi, Lodi, Italy

  • Pierpaolo Parogni,

    Roles Supervision

    Affiliation AAT Mantova - Agenzia Regionale Emergenza Urgenza (AREU) c/o ASST di Mantua, Mantua, Italy

  • Fabio Facchin,

    Roles Data curation

    Affiliation AAT Mantova - Agenzia Regionale Emergenza Urgenza (AREU) c/o ASST di Mantua, Mantua, Italy

  • Ugo Rizzi,

    Roles Supervision

    Affiliation AAT Cremona - Agenzia Regionale Emergenza Urgenza (AREU) c/o ASST di Cremona, Cremona, Italy

  • Daniele Bussi,

    Roles Data curation

    Affiliation AAT Cremona - Agenzia Regionale Emergenza Urgenza (AREU) c/o ASST di Cremona, Cremona, Italy

  • Simone Ruggeri,

    Roles Data curation

    Affiliation AAT Cremona - Agenzia Regionale Emergenza Urgenza (AREU) c/o ASST di Cremona, Cremona, Italy

  • Sabina Campi,

    Roles Supervision

    Affiliation AAT Varese - Agenzia Regionale Emergenza Urgenza (AREU) c/o ASST dei Sette Laghi, Varese, Italy

  • Paola Centineo,

    Roles Data curation

    Affiliation AAT Varese - Agenzia Regionale Emergenza Urgenza (AREU) c/o ASST dei Sette Laghi, Varese, Italy

  • Roberto De Ponti,

    Roles Supervision

    Affiliation ASST-Settelaghi, Ospedale di Circolo - Università dell’Insubria, Varese, Italy

  • Luigi Oltrona Visconti,

    Roles Supervision

    Affiliation Division of Cardiology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy

  • Simone Savastano ,

    Roles Conceptualization, Data curation, Formal analysis, Methodology, Supervision, Writing – original draft, Writing – review & editing

    s.savastano@smatteo.pv.it

    Affiliation Division of Cardiology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy

  •  [ ... ],
  • all the Lombardia CARe researchers

    Membership of the Lombardia CARe researchers is listed in the Acknowledgments.

  • [ view all ]
  • [ view less ]

Abstract

Background

The relationship between COVID-19 and out-of-hospital cardiac arrests (OHCAs) has been shown during different phases of the first pandemic wave, but little is known about how to predict where cardiac arrests will increase in case of a third peak.

Aim

To seek for a correlation between the OHCAs and COVID-19 daily incidence both during the two pandemic waves at a provincial level.

Methods

We considered all the OHCAs occurred in the provinces of Pavia, Lodi, Cremona, Mantua and Varese, in Lombardy Region (Italy), from 21/02/2020 to 31/12/2020. We divided the study period into period 1, the first 157 days after the outbreak and including the first pandemic wave and period 2, the second 158 days including the second pandemic wave. We calculated the cumulative and daily incidence of OHCA and COVID-19 for the whole territory and for each province for both periods.

Results

A significant correlation between the daily incidence of COVID-19 and the daily incidence of OHCAs was observed both during the first and the second pandemic period in the whole territory (R = 0.4, p<0.001 for period 1 and 2) and only in those provinces with higher COVID-19 cumulative incidence (period 1: Cremona R = 0.3, p = 0.001; Lodi R = 0.4, p<0.001; Pavia R = 0.3; p = 0.01; period 2: Varese R = 0.4, p<0.001).

Conclusions

Our results suggest that strictly monitoring the pandemic trend may help in predict which territories will be more likely to experience an OHCAs’ increase. That may also serve as a guide to re-allocate properly health resources in case of further pandemic waves.

Introduction

An increase in out-of-hospital cardiac arrest (OHCA) incidence has been observed during the first peak of the Coronavirus Disease-19 (COVID-19) pandemic, especially in the most affected countries [13]. This OHCAs’ increase has been explained both by infection-related causes and by pandemic-related causes. The most common infection-related causes are the rapid progression of respiratory failure [4], the pulmonary embolism [5], the myocarditis [6], the myocardial injury [7], the myocardial infarction due to a pro-thrombotic state [8] and the arrhythmias due to the SARS-CoV-2 infection and/or as side effect of drugs [9]. On the other hand, fear of in-hospital infection who prevented patients to call the Emergency Medical System (EMS) in case of effective need [10, 11] and the reduction in outpatient clinic visits [12, 13] represented the most common pandemic-related causes, leading to at home deterioration of time-dependent diseases. However, the increase in OHCA incidence has not been observed in all the countries [14], probably as a result of different pandemic burden. Moreover, albeit a correlation between OHCA incidence and COVID-19 case has been suggested both during the ascendant and the descendent phase of the first pandemic wave in the most affected countries [15], it is unknown whether this relationship could be present also during the second pandemic wave. The aim of our study was to analyse the trends of OHCA incidence and COVID-19 along the whole 2020 and to test the presence of a correlation between them during both the first and second pandemic wave at a provincial level.

Methods

Study population

The Lombardia Cardiac Arrest Registry (Lombardia CARe: NCT03197142) is a multicentre longitudinal prospective registry, which started enrolling all the OHCAs from the Province of Pavia from January 2015, and then was extended also to the Provinces of Lodi, Cremona, and Mantua from January 2019 and to the province of Varese since January 2020. All the data are collected according to the 2014 Utstein recommendations [16]. It was approved by the Ethical Committee of the Fondazione IRCSS Policlinico San Matteo (proc.20140028219), where the registry is hosted, and by all the others involved in the territory. According to the Ethics approval, the informed consent was required and signed only by patients who survived up to hospital discharge with a good neurologic outcome. The informed consent was waived for all the other cases and the data were anonymised.

For the present study, we considered all the OHCAs occurred in the provinces of Pavia, Lodi, Cremona, Mantua and Varese, in the Lombardy Region, in the northern Italy, after the first documented COVID-19 case in the Region (21st February 2020) and up to the 31st December 2020. The daily new cases and the cumulative incidence of COVID-19, based on the confirmed cases announced by the National Department of Civil Defence/Protezione Civile Nazionale [17], were also computed.

Setting and Emergency Medical System (EMS) description

The total area covered by the Lombardia CARe registry is of 9,061‬ km2 divided into the five provinces (Pavia 2,969 km2; Lodi 783 km2; Cremona 1,770 km2; Mantua 2,341 km2; Varese 1,198 km2). Each province has several rural regions and a few urban areas for a total population of 2,415,491 (Pavia 540,376; Lodi 227,412; Cremona 355,908; Mantua 406,919; Varese 884,876) as of January 1st, 2020.

In the entire Lombardy region, the emergency medical services is provided and coordinated by the “Agenzia Regionale Emergenza Urgenza (AREU)” which ensures the same procedures in all the region and collect all the electronic data from every rescue in a single data warehouse connected by the Lombardia CARe database.

According to the organization of AREU the rescue calls in the study territory are handled by two EMS dispatch centres: the “Sala Operativa Regionale Emergenza Urgenza (SOREU) della Pianura” based in Pavia and covering the provinces of Pavia, Lodi, Cremona and Mantua, and the “SOREU dei Laghi” based in Como and covering the provinces of Varese, Como and Lecco (the last two not already considered in the Lombardia CARe). The dispatch centres coordinates ambulances staffed with basic life support defibrillation (BLS-D)-trained personnel, and advanced life support (ALS)-trained staffed vehicles (a physician and a specialized nurse or a specialized nurse only). Moreover, five helicopters with a physician and a specialized nurse on board also serve the entire Lombardy region and other three can intervene from other neighboring regions. In case of suspected OHCA, the dispatcher activates one to three emergency vehicles (which may include a helicopter) with at least one physician and assists the calling bystander during chest compressions (telephone CPR). The decision about the attempt and the duration of resuscitation are left to the physician, whilst BLS-D-trained personnel are instructed to start resuscitation unless clear signs of death are present (rigor mortis, hypostasis, and injuries not compatible with life).

Data management

The data of the study are collected and managed using Research Electronic Data Capture (REDCap), a secure, web-based software platform, hosted at Fondazione IRCCS Policlinico San Matteo [18, 19].

Statistical analysis

We present categorical variables as counts and percentage. We present continuous variables using the median and 25th–75th interquartile range (IQR). Chi-squared test was use for the comparison of categorical variables. Kruskal-Wallis test was used to compared skewed continues variables in the different provinces.

We divided the study period in two equal portions: period 1 namely the first 157 days after the outbreak and including the first pandemic wave (from 21/02/2020 to 26/07/2020) and period 2 covering the second 158 days and including the second pandemic wave (from 27/07/2020 to 31/12/2020). We performed this choice considering both that an official date for both the end of the first pandemic wave and the start of the second one was not present and the following calculation of sample size. Basing on a previous paper from our group focused only on the first wave [15], where a correlation between the daily OHCA and COVID incidence with a coefficient of 0.4 was observed, assuming a possible decrease of the correlation strength due to the increase of the time frame (from 283 days in that paper to 313 in the present one), we calculated a sample size (N) required to determine a statistical significant correlation with a coefficient of at least 0.25. So, considering a type I (α) error of 0.05 and a type II (β) error of 0.2 and applying the following formula [standard normal deviate for α = Zα = 1.9600; standard normal deviate for β = Zβ = 0.8416; r = 0.25; C = 0.5 * ln[(1+r)/(1-r)] = 0.2554; N = [(Zα+Zβ)/C]2 + 3 = 123], the resulting minimum sample size was 123 days.

We calculated the cumulative, the daily and the 7-days incidence of OHCA expressed per 100,000 inhabitants both for the whole territory and for each province separately for both periods. We also calculated the cumulative, the daily and the 7-days incidence of COVID-19 cases per 100,000 inhabitants in the whole territory and in each province separately for both periods. We categorized the different Provinces at high-incidence or at low-incidence of COVID-19 cases depending on whether their cumulative incidences of COVID-19 cases per 100,000 inhabitants were over or beneath the cumulative incidence of the whole territory for each period. Via polynomial regression models we computed the Spearman R and its 95% confidence interval (CI) to measure the strength of the correlation between the daily incidence per 100,000 inhabitants of COVID-19 cases and the daily incidence per 100,000 inhabitants of OHCAs both for the entire series and for each province. Statistical analyses were performed with the MedCalc version 19.2 (MedCalc Software Ltd) and R version 4.0.4 (R foundation). All tests were two-sided, and a P-value<0.05 was considered statistically significant.

Results

COVID-19 cumulative incidence

The cumulative incidence of COVID-19 in the overall territory was 977.2/100,000 at the end of period 1 and 4875.8/100,000 at the end of period 2 (Fig 1A). Three provinces showed a high COVID-19 incidence in period 1 as they overcame the cumulative incidence of the whole territory: Cremona (1887.3/100,000), Lodi (1588.3/100,000) and Pavia (1038/100,000). On the contrary two provinces were at low COVID-19 incidence: Mantua (902.4/100,000) and Varese (449/100,000). As far as period 2 is concerned, only the province of Varese was at high COVID-19 incidence with 5401.8 case/100,000, overcoming the whole territory, whilst the other four provinces were at low incidence: Pavia (3521/100,000); Mantua (3089.3/100,000); Lodi (3082/100,000) and Cremona (2179/100,000).

thumbnail
Fig 1.

Panel A) COVID-19 cumulative incidence per 100,000 inhabitants in the whole territory and in each province separately in the period 1 and in period 2. Panel B) Trend of daily (blue continuous line) and 7-days (blue dotted line) OHCA incidence per 100,000 inhabitants and of daily (red continuous line) and 7-days (red dotted line) COVID-19 incidence per 100,000 inhabitants for the whole territory. Panel C) Relationship between COVID-19 daily incidence per 100,000 inhabitants (vertical axis) and OHCA daily incidence per 100,000 inhabitants (horizontal axis). The blue line indicates the linear regression and the red line indicates the polynomial regression. The grey area indicates the 95%CI.

https://doi.org/10.1371/journal.pone.0260275.g001

Patients’ characteristics among provinces and periods

The patients’ and OHCAs’ characteristics in the overall population as well as the comparison among the different provinces are presented in Table 1. Sex, EMS arrival time, the OHCA etiology, the OHCA location, the witnessed status, the rate of advanced resuscitation initiated, and the duration of resuscitation were slightly but significantly different amid provinces. On the contrary, the presenting rhythm, the rate of patients with resuscitation attempted and the rate of ROSC were similar. Concerning the comparison between patients’ and OHCAs’ characteristics in period 1 vs period 2, the home location of OHCA was higher in period 1, whilst the rate of both dispatcher-assisted CPR and of advanced resuscitation were higher during period 2 (Table 2). Comparing the provinces at high vs low COVID-19 incidence in the two periods, no differences were observed in patients’ and OHCAs’ characteristics in period 1. Significant differences were instead highlighted in period 2: COVID-19 high-incidence provinces showed OHCA more frequently located at home, more frequently unwitnessed and of medical etiology. Moreover, the rate of bystander-CPR and of advanced resuscitation initiated were significantly lower in high-incidence provinces during period 2 as well as the resuscitation duration (S1 Table).

thumbnail
Table 1. Patients’ and OHCAs’ characteristics in the overall population and comparison between the different provinces.

https://doi.org/10.1371/journal.pone.0260275.t001

thumbnail
Table 2. Comparison of the patients’ and OHCAs’ characteristics between the two periods.

https://doi.org/10.1371/journal.pone.0260275.t002

COVID-19 and OHCA trend and correlation

OHCA and COVID-19 daily and 7-days incidence per 100,000 inhabitants for the overall territory along the entire study period are presented in Fig 1B. A significant correlation between COVID-19 daily incidence and OHCA daily incidence was observed (R = 0.3, p<0.001 –Fig 1C). Considering the two periods separately, we found similar results as both in period 1 and in period 2: a significant correlation between the COVID-19 and the OHCA daily incidence was present in the entire territory (R = 0.4, p<0.001 for period 1 and 2) and only in the provinces at high COVID-19 incidence (period 1: Cremona R = 0.3, p = 0.001; Lodi R = 0.4, p<0.001; Pavia R = 0.3; p = 0.01; period 2: Varese R = 0.4, p<0.001). Similarly, in the two periods no significant correlation was found in the provinces at low COVID-19 incidence (period 1: Mantua R = 0.1, p = 0.26 and Varese R = 0.2, p = 0.25; period 2: Cremona R = 0.1, p = 0.43; Lodi R = 0.2, p = 0.2; Mantua R = 0.1, p = 0.6 and Pavia R = 0.2, p = 0.2) (Figs 2 and 3).

thumbnail
Fig 2.

Left side) Trend of daily (blue continuous line) and 7-days (blue dotted line) OHCA incidence per 100,000 inhabitants and of daily (red continuous line) and 7-days (red dotted line) COVID-19 incidence per 100,000 inhabitants for the whole territory and each provinces separately in period 1. Right side) Relationship between COVID-19 daily incidence per 100,000 inhabitants (vertical axis) and OHCA daily incidence per 100,000 inhabitants (horizontal axis) in the whole territory and in each provinces separately in period 1. The blue line indicates the linear regression and the red line indicates the polynomial regression. The grey area indicates the 95%CI.

https://doi.org/10.1371/journal.pone.0260275.g002

thumbnail
Fig 3.

Left side) Trend of daily (blue continuous line) and 7-days (blue dotted line) OHCA incidence per 100,000 inhabitants and of daily (red continuous line) and 7-days (red dotted line) COVID-19 incidence per 100,000 inhabitants for the whole territory and each provinces separately in period 2. Right side) Relationship between COVID-19 daily incidence per 100,000 inhabitants (vertical axis) and OHCA daily incidence per 100,000 inhabitants (horizontal axis) in the whole territory and in each provinces separately in period 2. The blue line indicates the linear regression and the red line indicates the polynomial regression. The grey area indicates the 95%CI.

https://doi.org/10.1371/journal.pone.0260275.g003

Discussion

The present study is the first one providing data about the correlation between OHCA and COVID-19 incidence during both the first and the second pandemic wave. We highlight that a significant correlation between the daily incidence of COVID-19 and the daily incidence of OHCA was observed in the whole territory and only in those provinces whose cumulative incidence of COVID-19 has overcome those of the whole territory. This trend was confirmed both during the first and the second pandemic period, suggesting the importance of the impact of the burden of COVID-19 cases in a given area on the occurrence of OHCAs. Despite the correlation was not strong, we believe that our results highlight the importance of strictly monitoring the contagion trend, not only in the entire territory, but also in the different provinces. This may help to predict which territories will be more likely to experience an increase of OHCAs incidence and may also serve as a guide to re-consider the allocation of the pre-hospital resources in case of future pandemic waves.

The raising of OHCA incidence has been described during the first pandemic wave, compared to the previous year, in regions highly burdened by COVID-19 [13, 10, 2022]. Such an increase was not observed in those regions with a low-incidence of COVID-19 cases [2327], suggesting a role of the pandemic burden probably due to both the direct and the indirect effects of the outbreak. Two of the main issues possibly related to that increase were the fear of in-hospital infection and the “surprise effect” of the pandemic on the healthcare systems, typical of the first pandemic wave [10]. It was unknown if information campaigns, adopted to increase citizens’ awareness to call EMS in case of effected need, and re-organization of EMS and healthcare systems [28, 29] may have prevented a similar increase during the second wave, avoiding the correlation between COVID-19 and OHCA incidence. Our study highlights that also in one of the first Region early affected by the pandemic, where the first case of COVID-19 in Europe was documented, and where multiple actions were adopted to mitigate the pandemic effect, an increase in OHCAs with a significant correlation between COVID-19 and OHCA incidence was observed also during the second pandemic wave, but only in high COVID-19 incidence provinces.

An interesting element worth to be deeply discussed and that could have potential practical implications in case of subsequent peaks, is that a major role seems to be played by the incidence of COVID-19 cases in the different provinces of our territory. Our study is indeed the first evaluating different areas, but encompassed in a homogenous region, served by the same EMS system, avoiding bias due to different organization of the pre-hospital ambulance system and hospital resources. We highlighted that during both the pandemic waves, the trend of OHCAs was significantly correlated to the trend of COVID-19 only in those provinces deeply affected by the pandemic, with an incidence of COVID-19 cases overcoming those of the whole territory analysed. Only few previous studies carried out so far on this topic have included an analysis on the different areas. A study performed in the United States [21] showed how the communities with a high COVID-19 mortality experienced a higher increase of OHCAs as compared to 2019 in contrast with the communities with a lower COVID-19 mortality. A correlation between COVID-19 cases and OHCA incidence has also been described by a previous study of our group carried out in the south part of the Lombardy Region [10] and by a study from London [22], both focused on the first 60 days of the pandemic. On the other hand, another study from Switzerland [30] didn’t highlighted an excess of OHCA in 2020 as compared to 2019. This was found also in those Cantons with a high incidence of COVID-19; however, albeit high for Switzerland, the incidence of COVID-19 in the Swiss Cantons was less than half of our high-incidence provinces, resembling the scene of our low-incidence provinces. Therefore, the lack of OHCA incidence increase in Switzerland could be attributed to the fact that that territories was not burdened by COVID-19 enough to cause an increase of OHCAs cases. All the studies discussed before were focused on the first pandemic wave, therefore it could be speculated that other factors could have affected the increasing of OHCA besides COVID-19 incidence, like as EMS organization, population’s characteristics and different beds and ICU hospital availability. Our study, covering both the first and the second pandemic wave, overcome this limitation highlighting that in provinces in the same region, with the same EMS organization and the same healthcare system, the increase of OHCA and the correlation between COVID-19 and OHCA incidence are evident only if the COVID-19 incidence is particularly higher.

The reasons for this evidence have to be sought in the direct and indirect effect of the pandemic [31]. In the areas deeply affected by COVID-19 outbreak, it is reasonable that there was an increase in OHCAs due to both cardiovascular and respiratory causes directly liked to SARS-CoV-2 infection [32, 33], caused by the increase in absolute number of the COVID-19 cases and, therefore, of the most critical clinical pictures. However, in these areas, there was also probably an increase of OHCA due to at-home deterioration of time-dependent diseases due to the fear of patients to in-hospital contagion, which prevented them to activate the EMS [10, 31]. This was probably more evident in these areas, compared to the low-incidence ones, as the population knew the load of people hospitalized for COVID-19 in the hospital of their province resulting in an increased fear of in-hospital contagion. Based on the above, we believe that our results could have great relevance. Considering indeed the risk of subsequent pandemic waves, mostly due to the spread of different variants of the virus [34, 35], and the future possible pandemic due to other viruses, the governments and healthcare politics will have to strictly monitor the contagion trend in order to be able to identify the territories higher at risk to be hit by a surge of OHCAs.

Regarding the patients’ and OHCAs’ characteristics, it is interesting to point out how the percentage of OHCAs occurred at home was higher during period 1 than in period 2. This observation can be the result of the stricter lockdown in period 1 than in period 2. Moreover, the dispatcher assisted-CPR was less provided in period 1 than in period 2 probably to prevent bystanders from contagion in the absence of defined knowledge of the new virus and of indication on how to safely perform CPR. Then, at the time of the second pandemic wave, some specific recommendations and guidelines were issued by the International Liaison Committee on Resuscitation (ILCOR) and other scientific societies [3638], reassuring the dispatchers about the safety and feasibility of telephone-CPR also during a pandemic. Similarly, also advanced resuscitation was attempted more in period 2 than in period 1, probably favoured by the increased availability of hospital resources during the second pandemic wave and by the satisfying results of advanced resuscitation experienced during the first phase [39]. These data can help to understand how the actions implemented to fight against the spread of pandemic, the different organizations during the two waves and the specific guidelines adopted after the first phase of COVID-19 pandemic may have played a role by modifying patients’ and OHCAs’ characteristics.

Our study has some limitations. The main limitation of our study is that the COVID-19 incidence was calculated on the confirmed cases announced by the National Department of Civil Defence. The number of cases is therefore strictly dependent on the number of Real-Time PCR test performed, which has considerably increased in the second compared to the first wave. However, this change in RT-PCR test performed has been homogenous in our territory as it is the same Region with the same healthcare system. Moreover, by considering the COVID-19 incidence in the different provinces compared to the whole territory separately for the first and second pandemic wave, we should have overcome this limitation. Probably, the number of deaths attributable to COVID-19 would be a data less exposed to change between the two waves, but, unfortunately, it is not available for every single province in our country as well as the number of patients admitted in the intensive care units. The second limitation is that, in this manuscript, we were not able to assess the number of patients with confirmed or suspected COVID-19 and also the outcome at hospital admission or discharge. The third limitation is that we cannot use 2019 data as comparison because the province of Varese was included in the LombardiaCARe in January 2020, therefore we have not historical data for this territory. However, we believe that the comparison of the provinces with the whole territory separately during the first and second pandemic wave can mitigate this limitation. Moreover, the comparison with 2019 was not an aim of the present study. Furthermore, the lacking of a control historical population do not allow to exclude with certainty that some of the differences observed in the two periods, and between high and low COVID-incidence provinces, are due to differences already present before the pandemic. However, considering that our population is quite homogeneous as all the territories pertains to the Lombardia Region, the data present in literature regarding the changes in OHCAs’ characteristics during the pandemic and, moreover, that the Emergency Medical System is the same for all the territories (Agenzia Regionale Emergenza Urgenza—Lombardia), we believe that our results are more likely representing differences bounded to the pandemic rather than the different provinces.

Conclusions

A correlation between the daily incidence of COVID-19 and OHCAs was observed only in the areas with a cumulative incidence of COVID-19 exceeding those of the whole territory. This was highlighted both during the first and the second pandemic wave. Our results suggest that strictly monitoring the contagion trend comparing every province to the whole territory may help to early identify those territories at higher risk of an increase of OHCA incidence. Our results may also serve as a guide to re-consider the allocation of the pre-hospital resources and to tailor information campaigns in the future pandemic waves.

Supporting information

S1 Table. Comparison of patients’ and OHCAs’ characteristics in the two periods according to the level of COVID-19 incidence (provinces with high-COVID incidence vs provinces with low-COVID incidence).

https://doi.org/10.1371/journal.pone.0260275.s002

(DOCX)

Acknowledgments

We would like to thank all the Lombardia CARe researchers and all the EMS personnel. EB, EC and SS are part of the European Resuscitation Council Research-Net. Lombardia CARe is a partner of the ESCAPE-NET consortium.

Lombardia CARe researchers: Fabrizio Canevari, Antonella Brancaglione, Andrea Pagliosa, Guido Matiz, Guido Francesco Villa, Maurizio Migliori, Antonella De Pirro, Simone Molinari, Vito Sgromo, Catherine Klersy, Valeria Musella, Martina Paglino, Francesco Mojoli, Moreno Curti, Stefano Buratti, Roberta Bertona, Livio Carnevale, Irene Raimondi Cominesi, Simone Ruggeri, Daniele Bussi, Erika Taravelli, Gian Battista Danzi, Cristian Fava, Giovanni Buetto, Alessandra Russo, Andrea Lorenzo Vecchi, Cecilia Fantoni, Cinzia Franzosi, Claudio Vimercati, Dario Franchi, Enrico Storti, Fulvio Giovenzana, Marco Botteri, Salvatore Ivan Caico, Matteo Caresani, Mario Luppi, Paola Genoni, Arianna Marioni, Ilaria Passarelli, Marco Paiella, Umberto Piccolo, Giovanna Perone, Gianluca Panni, Luca Bettari, Daniele Ghiraldin, Battistina Castiglioni, Luigi Moschini, Laura Zanotti, Diego Maffeo, Marco Cazzaniga.

References

  1. 1. Baldi E. et al., “Out-of-hospital cardiac arrest during the COVID-19 outbreak in Italy,” N. Engl. J. Med., vol. 383, no. 5, pp. 496–498, 2020, pmid:32348640
  2. 2. Marijon E. et al., “Out-of-hospital cardiac arrest during the COVID-19 pandemic in Paris, France: a population-based, observational study,” Lancet Public Heal., vol. 5, no. 8, pp. e437–e443, 2020, pmid:32473113
  3. 3. Prezant D. J. et al., “Characteristics Associated with Out-of-Hospital Cardiac Arrests and Resuscitations during the Novel Coronavirus Disease 2019 Pandemic in New York City,” JAMA Cardiol., vol. 5, no. 10, pp. 1154–1163, 2020, pmid:32558876
  4. 4. Wu C. et al., “Risk Factors Associated with Acute Respiratory Distress Syndrome and Death in Patients with Coronavirus Disease 2019 Pneumonia in Wuhan, China,” JAMA Intern. Med., vol. 180, no. 7, pp. 934–943, 2020, pmid:32167524
  5. 5. Poissy J. et al., “Pulmonary Embolism in Patients with COVID-19: Awareness of an Increased Prevalence,” Circulation, vol. 142, no. 2, pp. 184–186, 2020, pmid:32330083
  6. 6. Siripanthong B. et al., “Recognizing COVID-19–related myocarditis: The possible pathophysiology and proposed guideline for diagnosis and management,” Hear. Rhythm, vol. 17, no. 9, pp. 1463–1471, 2020, pmid:32387246
  7. 7. Shi S. et al., “Association of Cardiac Injury with Mortality in Hospitalized Patients with COVID-19 in Wuhan, China,” JAMA Cardiol., vol. 5, no. 7, pp. 802–810, 2020, pmid:32211816
  8. 8. Bangalore S. et al., “ST-Segment Elevation in Patients with Covid-19—A Case Series,” New England Journal of Medicine, vol. 382, no. 25. pp. 2478–2480, 2020, pmid:32302081
  9. 9. Vicentini A. et al., “QTc Interval and Mortality in a Population of SARS-2-CoV Infected Patients,” Circ. Arrhythm. Electrophysiol., vol. 13, no. 11, p. e008890, 2020, pmid:32991202
  10. 10. Baldi E. et al., “COVID-19 kills at home: The close relationship between the epidemic and the increase of out-of-hospital cardiac arrests,” Eur. Heart J., vol. 41, no. 32, pp. 3045–3054, 2020, pmid:32562486
  11. 11. De Rosa S. et al., “Reduction of hospitalizations for myocardial infarction in Italy in the COVID-19 era,” Eur. Heart J., vol. 41, no. 22, pp. 2083–2088, 2020, pmid:32412631
  12. 12. Fersia O. et al., “The impact of the COVID-19 pandemic on cardiology services,” Open Hear., vol. 7, no. 2, pp. 1–6, 2020, pmid:32855212
  13. 13. Colivicchi F., Di Fusco S. A., Magnanti M., Cipriani M., and Imperoli G., “The Impact of the Coronavirus Disease-2019 Pandemic and Italian Lockdown Measures on Clinical Presentation and Management of Acute Heart Failure: COVID-19 Pandemic and Heart Failure Management,” Journal of Cardiac Failure, vol. 26, no. 6. pp. 464–465, 2020, pmid:32417376
  14. 14. Lim Z. J., Ponnapa Reddy M., Afroz A., Billah B., Shekar K., and Subramaniam A., “Incidence and outcome of out-of-hospital cardiac arrests in the COVID-19 era: A systematic review and meta-analysis,” Resuscitation, vol. 157. pp. 248–258, 2020, pmid:33137418
  15. 15. Baldi E. et al., “Out-of-hospital cardiac arrest incidence in the different phases of COVID-19 outbreak,” Resuscitation, vol. 159. pp. 115–116, 2021, pmid:33385473
  16. 16. Perkins G. D. et al., “Cardiac arrest and cardiopulmonary resuscitation outcome reports: Update of the Utstein resuscitation registry templates for out-of-hospital cardiac arrest: A statement for healthcare professionals from a task force of the international liaison committee,” Circulation, vol. 132, no. 13, pp. 1286–1300, 2015, pmid:25391522
  17. 17. “Protezione Civile National Department.”
  18. 18. Harris P. A., Taylor R., Thielke R., Payne J., Gonzalez N., and Conde J. G., “Research electronic data capture (REDCap)-A metadata-driven methodology and workflow process for providing translational research informatics support,” J. Biomed. Inform., vol. 42, no. 2, pp. 377–381, 2009, pmid:18929686
  19. 19. Harris P. A. et al., “The REDCap consortium: Building an international community of software platform partners,” J. Biomed. Inform., vol. 95, Jul. 2019, pmid:31078660
  20. 20. Holland M. et al., “Excess Cardiac Arrest in the Community During the COVID-19 Pandemic,” JACC: Cardiovascular Interventions, vol. 13, no. 16. Elsevier Inc., pp. 1968–1969, 24-Aug-2020, pmid:32819492
  21. 21. Chan P. S., Girotra S., Tang Y., Al-Araji R., Nallamothu B. K., and McNally B., “Outcomes for Out-of-Hospital Cardiac Arrest in the United States during the Coronavirus Disease 2019 Pandemic,” JAMA Cardiol., vol. 6, no. 3, pp. 296–303, 2021, pmid:33188678
  22. 22. Fothergill R. T., Smith A. L., Wrigley F., and Perkins G. D., “Out-of-Hospital Cardiac Arrest in London during the COVID-19 pandemic,” Resusc. Plus, vol. 5, no. November, p. 100066, 2021, pmid:33521706
  23. 23. Ball J., Nehme Z., Bernard S., Stub D., Stephenson M., and Smith K., “Collateral damage: Hidden impact of the COVID-19 pandemic on the out-of-hospital cardiac arrest system-of-care,” Resuscitation, vol. 156, no. September, pp. 157–163, 2020, pmid:32961304
  24. 24. Elmer J., Okubo M., Guyette F. X., and Martin-Gill C., “Indirect effects of COVID-19 on OHCA in a low prevalence region,” Resuscitation, vol. 156. pp. 282–283, 2020, pmid:32920116
  25. 25. Sayre M. R. et al., “Prevalence of COVID-19 in Out-of-Hospital Cardiac Arrest: Implications for Bystander Cardiopulmonary Resuscitation,” Circulation, vol. 142, no. 5, pp. 507–509, 2020, pmid:32496811
  26. 26. Paoli A., Brischigliaro L., Scquizzato T., Favaretto A., and Spagna A., “Out-of-hospital cardiac arrest during the COVID-19 pandemic in the Province of Padua, Northeast Italy,” Resuscitation, vol. 154. pp. 47–49, 2020, pmid:32653572
  27. 27. Uy-Evanado A. et al., “Out-of-Hospital Cardiac Arrest Response and Outcomes During the COVID-19 Pandemic,” JACC Clin. Electrophysiol., vol. 7, no. 1, pp. 6–11, 2021, pmid:33478713
  28. 28. Marcon E. et al., “Schiavonia Hospital response to COVID-19 outbreak: a first single-center experience,” Ann. Ist. Super. Sanita, vol. 56, no. 3, pp. 365–372, 2020, pmid:32959803
  29. 29. Carugo S. et al., “Management of acute coronary syndromes during the COVID-19 outbreak in Lombardy: The ‘macro-hub’ experience,” IJC Hear. Vasc., vol. 31, 2020, pmid:33173807
  30. 30. Baldi E. et al., “Out-of-hospital cardiac arrests and mortality in Swiss Cantons with high and low COVID-19 incidence: A nationwide analysis,” Resusc. Plus, vol. 6, p. 100105, 2021, pmid:34223367
  31. 31. Tan H. L., “How does COVID-19 kill at home and what should we do about it?,” Eur. Heart J., vol. 41, no. 32, pp. 3055–3057, 2020, pmid:32882708
  32. 32. Lüscher T. F., “Understanding COVID-19: In the end it is the endothelium-what else?,” Eur. Heart J., vol. 41, no. 32, pp. 3023–3027, 2020, pmid:33216863
  33. 33. Price S. et al., “Respiratory management in severe acute respiratory syndrome coronavirus 2 infection,” Eur. Hear. journal. Acute Cardiovasc. care, 2020, pmid:32375488
  34. 34. Conti P. et al., “The british variant of the new coronavirus-19 (Sars-cov-2) should not create a vaccine problem,” J. Biol. Regul. Homeost. Agents, vol. 35, no. 1, pp. 1–4, 2021, pmid:33377359
  35. 35. Nakamichi K. et al., “Hospitalization and mortality associated with SARS-CoV-2 viral clades in COVID-19,” Sci. Rep., vol. 11, no. 1, pp. 1–11, 2021,
  36. 36. Couper K. et al., “COVID-19 in cardiac arrest and infection risk to rescuers: A systematic review,” Resuscitation, vol. 151. pp. 59–66, 2020, pmid:32325096
  37. 37. Nolan J. P. et al., “European Resuscitation Council COVID-19 guidelines executive summary,” Resuscitation, vol. 153, no. June, pp. 45–55, 2020, pmid:32525022
  38. 38. Edelson D. P. et al., “Interim Guidance for Basic and Advanced Life Support in Adults, Children, and Neonates with Suspected or Confirmed COVID-19: From the Emergency Cardiovascular Care Committee and Get with the Guidelines-Resuscitation Adult and Pediatric Task Forces of the,” Circulation, pp. E933–E943, 2020, pmid:32270695
  39. 39. Baldi E. et al., “Treatment of out-of-hospital cardiac arrest in the COVID-19 era: A 100 days experience from the Lombardy region,” PLoS One, vol. 15, no. 10, 2020, pmid:33091034