Early indicators of intensive care unit bed requirement during the COVID-19 epidemic: A retrospective study in Ile-de-France region, France

By the COVID-19 APHP-Universities-INRIA-INSERM Group

doi:10.1371/journal.pone.0241406

. 2020 Nov 18;15(11):e0241406. doi: 10.1371/journal.pone.0241406

Early indicators of intensive care unit bed requirement during the COVID-19 epidemic: A retrospective study in Ile-de-France region, France

By the COVID-19 APHP-Universities-INRIA-INSERM Group^¶,^*

Editor: Corstiaan den Uil¹

PMCID: PMC7673527 PMID: 33206660

Abstract

The aim of our retrospective study was to evaluate the earliest COVID19-related signal to anticipate requirements of intensive care unit (ICU) beds. Although the number of ICU beds is crucial during the COVID-19 epidemic, there is no recognized early indicator to anticipate it. In the Ile-de-France region, from February 20 to May 5, 2020, emergency medical service (EMS) calls and the response provided (ambulances) together the percentage of positive reverse transcriptase polymerase chain reaction (RT-PCR) tests, general practitioner (GP) and emergency department (ED) visits, and hospital admissions of COVID-19 patients were recorded daily and compared to the number of ICU patients. Correlation curve analysis was performed to determine the best correlation coefficient, depending on the number of days the indicator has been shifted. Primary endpoint was the number of ICU patients. EMS calls, percentage of positive RT-PCR tests, ambulances used, ED and GP visits of COVID-19 patients were strongly associated (R² ranging between 0.79 to 0.99, all P<0.001) with COVID-19 ICU patients with an anticipation delay of 23, 15, 14, 13, and 12 days respectively. Hospitalization did not anticipate ICU bed requirement. A qualitative analysis of the onset of the second wave period of the epidemic (August 1 to September 15, 2020) in the same region provided similar results. The daily number of COVID19-related telephone calls received by the EMS and corresponding dispatch ambulances, and the proportion of positive RT-PCR tests were the earliest indicators of the number of COVID19 patients requiring ICU care during the epidemic crisis, rapidly followed by ED and GP visits. This information may help health authorities to anticipate a future epidemic, including a second wave of COVID19, or decide additional social measures.

Introduction

The COVID-19 pandemic has a high impact on public health in many countries [1]. The medical response has combined all hospital resources, including emergency departments (ED), conventional hospitalization, and intensive care units (ICU). Despite the beginning of the epidemic in China [2], most Western countries were not sufficiently prepared for its intensity and particularly the wave of critically ill patients requiring ICU. Except for some countries which succeeded in early control of epidemic transmission chains (South Korea, Hong Kong) [3, 4], most countries (China, Italy, France, Spain, UK, USA and Brazil) experienced a rapidly diffusing epidemic pattern. It strucked the health care system with a rare violence and threatened possible ICU bed shortage which would have led to additional mortality [5–7]. Although epidemiological analyses provided accurate early information concerning the progression of the epidemic [8], they were not able to predict its evolution at the peak of the crisis (including the number of ICU beds required). The peak of the crisis depends indeed on collective measures (testing, isolation of infected patients, social distancing, wearing mask, hand washing, and lockdown), which are the only actions with proven efficacy in the absence of proven specific treatment and/or vaccination to date [9]. In France, all patients requiring ICU were admitted in such units, but this result was only obtained by a considerable increase in the number of ICU beds, and inter-regional ICU patient transfers, to avoid overwhelming of local ICUs [10].

The aim of our study was to evaluate what would have been the earliest COVID19-related signals to anticipate ICU beds requirements. Because several days elapsed between the onset of clinical symptoms and worsening in a small proportion of infected patients requiring ICU (estimated around 5%) [11], we hypothesized that such early signals exists and may be helpful for both public health policy or decisions and hospital management. Thus, we investigated the telephone calls received by the emergency medical services (EMS) and the immediate response provided, visits to general practitioners (GP) and emergency department (ED), hospital in-patient admissions, and positive reverse transcriptase polymerase chain reaction (RT-PCR) tests. We think that this analysis could help health care systems to more rapidly adapt to a future epidemic, including a second wave of COVID-19 [12]. These indicators may help health authorities to decide additional measures such as new lockdown or any other preventive measures at the population level.

Material and methods

This study was approved by the Sorbonne Université ethical committee (CER N° 2020–55, Paris, France) which authorized waived informed consent.

The Ile de France Region (12.1 million inhabitants) comprises eight administrative sub-entities, each of them served by a medicalized EMS known as SAMU (Service d’Aide Médicale Urgente). The Paris city and its inner suburbs (6.7 million inhabitants) is covered by 4 SAMUs belonging to the Greater Paris University hospital network APHP (S1 Fig). The individual SAMUs operate identically, use the same health information and management system (Centre d’Appel de Régulation MEdicale Nominal (CARMEN) created in 2010) and provide an adapted answer to calls to “15”, the French toll-free number dedicated to medical emergencies. This service is based on a medical response to emergency calls where an emergency physician decides the appropriate response for each case. Depending on the evaluation of the severity of the case and the circumstances, the phone response may be a medical advice, a home visit of a GP, the dispatch of an ambulance or rescue workers, and, in the most serious cases, sending a mobile intensive care unit (MICU) staffed by an emergency physician sent on scene as a second or a first tier [13]. To cope with the surge of calls during the COVID-19 crisis, the 4 SAMUs involved in the study have increased their response capacity by creating specific procedures for COVID-19-related calls, such as staff increase, dedicated computer stations, interactive voice server, video consultation, sending instructions by SMS. Prehospital EMT and MICU teams were also significantly reinforced. Since January 20, 2020 all calls and patient records related to COVID-19 were identified in their information system and a daily automated activity report was produced.

The primary endpoint was the number of COVID19 patients who were present in ICU in the Ile de France region during the study period (from February 20 to May 5, 2020), excluding patients transferred in other regions. The secondary endpoint was the daily number of new COVID19 patients admitted into ICU. During the study period, APHP staffed a regionalized and dedicated team to ensure that information concerning ICU bed availability was accurate and available in real time (Répertoire Opérationnel des Ressources Ile-de-France; https://www.ror-if.fr/ror/) and could help any physician to rapidly find an ICU bed for a given patient [10]. We collected daily the number of ICU patients from the Système d’Information pour le Suivi des Victimes (SI-VIC) database which provides real time data on the COVID-19 patients hospitalized in French public and private hospitals (https://www.data.gouv.fr) and was activated for COVID-19 epidemic on March 13, 2020. Before that date, the number of ICU patients was collected by a direct centralized survey of the Regional Health Agency. All COVID-19 ICU cases were confirmed by RT-PCR or computed tomographic scan suggestive of SARS-CoV-2 infection.

We studied 6 indicators as they were reliable and accessible on a daily basis: 1) number of emergency calls received by the 4 SAMUs of APHP and diagnosed as suspected COVID19 patients, using the CARMEN database (S1 Table). A clinical protocol was employed, according to the French ministry of health recommendations [14]; 2) number of these patients requiring dispatch of an ambulance (either ordinary or MICU) using the CARMEN database (S1 Table); 3) number of ED visits for a clinically suspected diagnosis of COVID-19 infection in the Ile-de-France Region, using the French OSCOUR® health information system, created in 2004 and which connects all French ED (https://www.data.gouv.fr). 4) the number of COVID-19 diagnoses made by a private network of GPs who performed only emergency visits on a 24 hour and 7 day basis at home (SOS médecins), in the Ile-de-France region (https://www.data.gouv.fr); 5) the number of hospital admission of the COVID19 patients, in the Ile-de-France region; 6) percentage of positive RT-PCR tests for COVID19 in the Ile-de-France region (https://www.data.gouv.fr). Only the percentage of positive results was considered because the availability of biological tests was limited during the early phase of the epidemic. Moreover, during this early phase, only the APHP could perform RT-PCR. Publicly available sources and APHP data warehouse produced data with de-identified information.

Lastly, since these indicators are now used in our region on a routine daily basis, we assessed their value during the onset of the second wave of the COVID-19 epidemic which hits France. Thus, we analysed the period from August 1 to September 15, 2020 to provide an external validation using a second time frame in the same region. Only a qualitative analysis could be performed since data on primary and secondary endpoints are obviously truncated during this very early phase.

Statistical analysis

For each indicator, we determined the onset defined as the first day the indicator became greater than 0, 50% increase, and peak of the curve during the ascension phase. For these three points, we calculated delays as compared to endpoints. We performed correlation curve analysis during the whole study period by plotting (ICU patients at date T) vs (value of the indicator at date T+t) and varying t (time lag expressed as number of days), to determine the best correlation coefficient, depending on the number of days the indicator had been shifted. The primary variable chosen to assess time delay was the correlation curve, and a time lag value ≥ 7 days was considered as an early alert indicator, and ≥ 14 days a very early one. For each indicator, we computed the time-dependant reproduction ratio (R(t)) using a gamma-distributed generation interval distribution with mean 6 days and standard deviation 4 days [15] to verify that they actually behave as coherent indicators of the epidemic.

We retrospectively investigated how these indicators could have been used as tools to anticipate the burden of ICU COVID19 patients. Since the initial capacity of ICU beds was 40% of that reached at the peak of the crisis we decided to fix this 40% threshold as the upper limit for each indicator, as previously reported [16], and half of this threshold (20%) was made the lower acceptable limit, delimiting a red zone above 40%, a green zone below 20% and an orange zone between these two limits. In addition, we also defined the slope for each indicator that correspond to the 40 and 20% of the maximum slope reached during the initial raise, using the same colour-coding.

Data are expressed as medians [interquartile IQR], or number (percentage). Correlation between two variables was assessed using the parametric Pearson test and expressed as a correlation coefficient. A P value of less than 0.05 was considered significant.

Results

The daily number of emergency calls received by the EMS, ambulances sent, GP and ED visits, RT-PCR, and hospital admission in all patients and COVID-19 patients during the study period is shown in S1 Table. The number of ICU beds increased from 1189 to 2945 (248%) and the number of COVID ICU patients ranged from 0 to 2677 (91%). There was a high correlation between the numbers of ICU patients and new ICU COVID19 patients in the Ile-de-France region and in APHP hospitals (R² = 0.99, P<0.0001 and R² = 0.96, P<0.0001, respectively). Inter-regional transfers of ICU patients was performed only lately (from April 28 to May 4, 2020) and concern a few proportion of all patients admitted into ICU during the study period (252/2863, 3.9%).

Fig 1 shows the comparison of each indicator to the primary (number of ICU patients) and secondary (number of new ICU patients) endpoints. The number of EMS calls, number of ambulance dispatch, and percentage of positive RT-PCR tests, were very early indicators, followed by diagnosis by GP and admission to the ED (Table 1). Correlation curve analysis is shown in S2 and S3 Figs. Fig 2 shows what happened if a semi-quantitative analysis of these indicators and their respective thresholds and slopes had been applied during the initial phase of the epidemic.

Table 1. Observed delays between each indicator and primary or secondary endpoints.

Indicator	Onset	50% of the peak	Peak	Correlation curve
	Delay	Delay	Delay	Delay	R²
	(Day)	(Day)	(Day)	(Days)	R²
Primary endpoint: Number of ICU patients
EMS calls	16	18	26	23	0.89
% of positive RT-PCR	11	12	17	15	0.95
Ambulances	12	5	12	14	0.79
ED visits	5	5	12	13	0.84
GP visits	3	8	11	12	0.85
Hospital admission	0	0	-5	-1	0.99
Secondary endpoint: Number of new ICU patients
EMS calls	16	15	13	13	0.83
% of positive RT-PCR	11	9	4	4	0.84
Ambulances	12	5	-1	5	0.87
ED visits	5	2	-1	1	0.81
GP visits	3	5	-2	4	0.91
Hospital admission	0	-3	-18	-10	0.79

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Correlation curve analysis between each indicator and the number of intensive care unit (ICU) patients was performed during the whole study period (See Methods). R²: Pearson coefficient of correlation. EMS: emergency medical system; GP: general practitioner; ED: emergency department.

Fig 2 — Since initial capacity of ICU bed was 40% of the one reached at the peak of the crisis, a red zone was defined above this threshold, a green zone below half of this threshold (i.e. 20% of ICU bed maximum capacity), and an orange zone between these two limits. We also defined the slope for each indicator that correspond to the 40 and 20% of the maximum slope reached during the initial raise, using the same colour coding. The first red flags would have occurred on February 24 (slope) and March 4 (threshold) for COVID19 EMS calls, 22 and 13 days before the date of the French lockdown (March 17, 2020).

The time-dependant reproduction ratio confirmed that the number of EMS calls informed early on the epidemic course (Fig 3). Similar information was obtained from other indicators with delays up to 15 days, indicating that they appropriately and accurately described the evolution of the epidemic.

As an external validation, the onset of the second wave period in the same region provided similar qualitative results (S4 and S5 Figs). EMS calls, percentage of positive RT-PCR tests, ambulances used, ED and GP visits of COVID-19 patients again the number of COVID-19 ICU patients, although anticipation delays could not be calculated because of the censored data of primary and secondary endpoints.

Discussion

The present study shows that five indicators (EMS calls, percentage of positive RT-PCR tests, dispatch ambulances, ED and GP visits) anticipated the burden of ICU patients for at least 7 days during the COVID19 epidemic, the first three by at least 14 days. This result suggests that they could be valuable tools as daily alert signals to set up plan to face the outbreak burden during the initial wave of the epidemic and may possibly also work during the second wave. These results are important since mortality has been reported being correlated to health care resources [17].

Although many studies estimated the number of patients who would have severe COVID-19 [18, 19] and a recent study analysed the emergency calls and ambulance dispatch [13], very few have assessed early signals associated with ICU requirements. These studies investigated internet or social media data [20–22]. To our knowledge, no study analysed data from COVID-19 suspected or infected patients [23]. Since several days (estimation 7–12 days) elapse between the onset of clinical symptoms and worsening of clinical status requiring either admission to the hospital or ICU, our hypothesis is that indicators exist that could be easily available on a daily basis, enabling an accurate and anticipated survey of the COVID-19 epidemic. The availability on a defined geographical region rather than a whole country is important since a heterogeneous time and space spread of the SARS-CoV2 virus has been observed in all severely impacted countries.

Among all tested indicators, EMS calls for COVID19 was a very early one. As many countries have this type of health care organization for emergency calls, the use of this signal is widely applicable although political incitation to use this canal for the population to signal COVID19 infection may differ from one country to another. However, once appropriately used this indicator is early and sensitive. The observed delay between EMS calls and admission into ICU concords with those reported for worsening of the disease [24]. Moreover, the medical assessment of emergency calls may be improved by learning from the first wave. The dispatch of ambulances by EMS was also a very early indicator. It should be pointed out that health authorities initially recommended symptomatic patients not to come directly to ED but rather to call the EMS which were instructed to only transport to hospitals patients needing hospital care and to refer others to GP if ambulatory care was needed. The inclusion of MICU ambulances may have also introduced a bias since some of these severe patients were directly admitted into ICU. Consequently, in different EMS systems, we cannot exclude that transport to the ED may behave differently. The importance of EMS as an early indicator of non-infectious medical disaster has already been reported during the 2003 heatwave in France [25].

The third very early indicator was the proportion of positive RT-PCR for COVID19. Many countries as well as the WHO have emphasized the importance of an early detection of the SARS-CoV2 virus by molecular diagnosis [26]. South Korea and Germany in Europe have widely used extensive testing to better control the epidemic [27, 28]. It is therefore not surprising that this test appears as an early indicator for the COVID19 epidemic. Moreover, when testing is performed on a large scale to detect not only infected patients but also contact individuals, the signals provided by positive RT-PCR may occur earlier, which was not the case in France at the time of the first wave. The population tested further evolved with the course of the epidemic, particularly the proportion of positive patients admitted to the hospital. In France, because of initial test shortage, RT-PCR was reserved for hospitalized patients, including ICU patients, and health staff. As the epidemic diminished and RT-PCR availability increased, more tests were performed for outpatients and their respective contacts to decrease epidemic chain transmission in France. In this situation, the positive RT-PCR may become an earlier test, since some additional time elapses (estimated 3–5 days) between contamination and onset of symptoms. Further studies are required to investigate that point.

Other early indicators were the number of COVID19 diagnosis made by GP and ED. As hospitals were seen by patients as potentially dangerous, many of them were not prompted to attend the ED which led to a dramatic decrease in ED visits for any causes in France during the COVID19 epidemic, as previously reported [29]. The use of GP diagnosis is a valuable tool if a national or regional system uploads valuable and structured information in real time. France converted an existing system for an influenza epidemic, to COVID-19 survey [30], but the number of involved GP remained relatively low and, as they are not available 24 hour a day, they could only enrolled a limited number of patients. The French mobile GP network SOS Médecins offers an alternative for our purpose since their members recorded appropriate clinical information concerning COVID-19 patients during the study period. This network has been included in a national process survey of epidemics since many years. Our results can be applicable to other countries when such organization is at work or when alternative and reliable GP based clinical data is collected. In addition, the French health care authorities have now promoted all GPs as key actors in the detection of COVID clusters. Therefore, evaluation and survey of GP visits should certainly become more sensitive.

Hospital admission ended up not being an early indicator of the number of ICU patients. This result concords with previous studies reporting that the delay between hospital admission to admission into ICU is closed to one day [31].

Several limitations of this study should be noted. First, although the sample sizes were large, our observation was limited to one region of France (with a very high population density) and one event and thus extrapolation should be interpreted with caution. Nevertheless, our analysis of the onset of the second wave of the epidemic in the same region provide can be interpreted as a first external validation evidence, although the qualitative threshold initially chosen (Fig 2) should be adjusted, at least for EMS calls (S5 Fig). Second the geographical repartition and population was not identical between some indicators and endpoints (S1 Fig). However, there was a very high correlation between ICU patients in the region and in APHP as the regulation of ICU bed availability was regionalized during the epidemic and based at the APHP. The GP indicator only reflects a particular activity (emergency visits at home) which is not distributed everywhere (less in rural areas) but this was the only accessible GP indicator. Because of biological test shortage, we only looked at the proportion of positive RT-PCR tests but the absolute number should probably be preferred in countries without such limitation. The presence of physicians (telemedicine) and not only emergency medical technicians (EMT) characterizes the French EMS model. Nevertheless, there is no indication suggesting that an EMT-based system using scripts may not lead to comparable results, particularly during an epidemic wave with a high prevalence of the disease. Definition of COVID-19 suspected diagnosis may have slightly varied during the study period in EMS, ED, and GP and between physicians. Admission into ICU has been modified during the study period as intensivists better understood the characteristics of the disease and modified their therapeutic approaches, particularly trying to avoid tracheal intubation [32], and transfers of ICU patients outside the region was performed just before the peak but these transfers occurred during the very last days before the peak and concerned only a low proportion (3.9%) of patients. We did not perform a multivariate analysis for two reasons: 1) the time delay between each indicator and the endpoint (ICU patients) markedly varied and thus a multivariate indicator should not provide an early alert; in contrast, having several indicators with varying time delay provide a series of successive alerts which reinforce each other (Fig 2 and S5 Fig). Lastly, the raw signal of the indicators was sometimes noisy and a more advanced mathematical analysis could improve their performance [33]. Despite these limitations, we consider that our comparisons remain valid and could be adapted to most health systems and potentially to other types of epidemic scheme.

Conclusions

The daily number of COVID19-related telephone calls received by the EMS and corresponding ambulance dispatch, and the proportion of positive RT-PCR were the earliest indicators of the number of COVID19 patients requiring ICU care during the epidemic crisis in the Ile-de-France region, rapidly followed by ED and GP visits. This information may help health authorities to anticipate a future epidemic, including a second wave of COVID19, to monitor lockdown exit and decide additional social measures to better control COVID-19 outbreak.

Supporting information

S1 Fig. The Ile-de-France region.

This French region (12·1 million inhabitants) comprises eight administrative sub-identities, indicated by their number on the map, the town of Paris being 75. In the present study, the numbers of emergency departments (ED) visits, positive reverse transcriptase polymerase chain reaction (RT-PCR) tests, hospital admissions, intensive care unit (ICU) patients, and new ICU patients were obtained from the whole region. A regionalized organization was installed enabling to rapidly find an ICU bed for a given patient wherever the patient was initially admitted. Panel A: Data from emergency medical system (EMS), including emergency calls and dispatch of ambulances were obtained from the Paris city (75) and its inner suburbs which comprise four administrative sub-entities (75, 92, 93, 94) and their respective EMS (6·71 million inhabitants). Panel B: Data from general practitioner (GP, SOS Médecins network) were obtained from the Ile-de-France region but the density of activity of this GP network (expressed as number of annual visits per million inhabitants).is heterogeneous within the Ile-de-France region.

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S2 Fig. Primary endpoint.

Correlation curves of the six tested indicators compared to the number of intensive care unit (ICU) patients during the study period. EMS: emergency calls; GP: general practitioner; ED: emergency department. D: delay (in days) between the two variables. R²: Pearson coefficient of correlation.

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S3 Fig. Secondary endpoint.

Correlation curves of the six tested indicators compared to the number of new intensive care unit (ICU) patients during the study period. EMS: emergency calls; GP: general practitioner; ED: emergency department. D: delay (in days) between the two variables. R²: Pearson coefficient of correlation.

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S4 Fig. Evolution of the 6 tested indicators compared to the number of intensive care unit (ICU) patients and number of new ICU patients during the onset of the second epidemic waver (from August 1 to September 15, 2020).

EMS: emergency calls; GP: general practitioner; ED: emergency department; RT-PCR: reverse transcriptase polymerase chain reaction tests.

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S5 Fig. Dashboard of the retained indicators (EMS calls, percentage of positive reverse transcriptase polymerase chain (RT-PCR) tests, and general practitioners (GP) visits) during the second wave of the epidemic (August 1 to September 15, 2020) using the same definition as in Fig 2.

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S1 Table. Number of emergency calls per day related to COVID-19 patients received by the four emergency medical system (EMS, i.e. SAMU) of the Assistance Publique-Hôpitaux de Paris (APHP) and number of these patients requiring dispatch of an ambulance (either ordinary or mobile intensive care unit (MICU)) from January 27 to September 15, 2020.

(DOCX)

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S2 Table. Median daily number of emergency calls received by the emergency medical system, ambulances sent, general practitioner (GP) visits, emergency department (ED) visits, reverse transcriptase polymerase chain transmission (RT-PCR), and hospital admission in al; patients and COVID-19 patients during the study period.

(DOCX)

Click here for additional data file.^{(14.9KB, docx)}

Acknowledgments

We thank Dr. David Baker, DM, FRCA, (Department of Anesthesiology and Critical Care, Hôpital Necker-Enfants Malades, Paris, France) for reviewing the manuscript. The authors are deeply grateful to Audrey Bourdette, Arthur Cornet, Alban Jourdain, Côme Cheritel, and Henri Matalon from the Data science team, Department of Strategy and transformation at APHP (Paris, France) for their essential assistance for data collection, analysis, editing of figures and statistical analysis. We thank Pr. Laurent Tréluyer (Department of informatics and computer sciences, APHP, Paris, France) for providing us data and Dr. Christophe Leroy (APHP, Paris, France) for valuable discussion. List of investigators by alphabetical order: Prof. Frédéric Adnet (Université Paris 13, Institut National de la Santé et de la Recherche Médicale [INSERM], SAMU 93, Assistance Publique-Hôpitaux de Paris [APHP], Bobigny, France); Marin Boyet (Institut National de la Recherche en Informatique et en Automatique [INRIA], Ecole polytechnique, and Centre National de la Recherche Scientifique [CNRS], Paris, France); Dr. Jean-Jacques Avrane (SOS médecins, Paris, France); Prof. Frédéric Batteux (Université de Paris and APHP, Paris, France); Prof. Pierre-Yves Boëlle (Sorbonne Université, INSERM, and APHP, Paris, France); Dr. Jérémie Boutet (SAMU 92, APHP, Garches, France) Prof. Vincent Calvez (Sorbonne Université, INSERM, and APHP, Paris, France); Prof Pierre Carli (SAMU zonal, APHP and Université de Paris, Paris, France); Dr. Pascal Chansard (SOS Médecins, Paris, France); Dr. Charlotte Chollet (SAMU 94, APHP, Créteil, France); François Crémieux (Deputy CEO, APHP, Paris, France); Pr. Diane Descamps (Université de Paris, INSERM, and APHP, Paris, France); Prof. Stéphane Gaubert (INRIA, Ecole polytechnique, and CNRS, Paris, France); Dr. Laurent Goix (SAMU 93, APHP, Bobigny, France); Pr. Pierre Hausfater (Sorbonne Université and APHP, Paris, France); Martin Hirsch (CEO APHP, Paris, France); Dr. Eric Lecarpentier (SAMU 94, APHP, Créteil, France); Dr. Thomas Loeb (SAMU 92, APHP, Garches, France); Dr. Jean-Sébastien Marx (SAMU 75, APHP, Paris, France); Prof. Catherine Paugam (Université de Paris, INSERM, and APHP, Clichy, France); Prof. Renaud Piarroux (Sorbonne Université, INSERM, and APHP, Paris, France); Prof. Bruno Riou (Sorbonne Université, INSERM, and APHP, Paris, France); Prof. Remi Salomon (Université de Paris and APHP, Paris, France); Dr. Serge Smadja (SOS Médecins, Paris, France); Dr. Caroline Telion (SAMU 75, APHP, Paris, France); Prof. Antoine Vieillard-Baron (Université Versailles St Quentin and Yvelynes and APHP, Boulogne, France).

Data Availability

Raw data are publicly available (https://www.data.gouv.fr) or provided in the Supporting Information file.

Funding Statement

No specific funding was received for this study.

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PLoS One. doi: 10.1371/journal.pone.0241406.r001

Decision Letter 0

Corstiaan den Uil

16 Sep 2020

PONE-D-20-23904

Emergency calls are early indicators of ICU bed requirement during the COVID-19 epidemic.

A retrospective study in Ile-de-France region, France

PLOS ONE

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Reviewer #1: Partly

Reviewer #2: Partly

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Reviewer #1: I Don't Know

Reviewer #2: Yes

**********

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Reviewer #2: Yes

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Reviewer #1: Summary of the manuscript:

The authors retrospectively investigated predictors for ICU capacity needs due to COVID-19 in the Ile-de-France region, France, between February 20 and May 5, 2020. Indicators that were found to be correlated with ICU needs (with a delay) included EMS calls, percentage of positive RT-PCR tests, GP visits, ED visits and ambulances.

This is an extremely important question for the preparation of a potential second wave of COVID-19 in the region, and the authors have argued its importance well in the introduction. However, I feel the conclusions could have been much stronger had authors treated this research question as a prediction problem, and use appropriate prediction methods to address this problem. Treating this as a prediction problem and validate the prediction tool in an external sample would help with generalizing the result to the future and inform practice. Please see my major comments below.

Major comments and suggestions to the authors:

1. The correlation between many indicators and the outcomes may not be generalizable to the future (which is the main goal of this paper). For example, the authors used % test positive as one indicator, but % test positive, although an indicator for the spread of SARS-CoV-2 in the community during the early phases of the pandemic (due to testing shortage), in many settings it is starting to become an indicator of testing availability and how widespread testing was, rather than an indicator for community spread. Even if % test positive could indicate ICU capacity needs in the early phases of the pandemic in the Ile-de-France regioon, its correlation with ICU needs is unlikely to stay the same during a second wave of COVID-19. Similar arguments can be made for other indicators as well, where policy and resource availability changes can potentially change the correlation between an indicator and the outcome.

• Suggestion: use an external validation dataset (either another time frame, or data from another region) to validate any prediction rules the authors concluded. For example, the authors concluded that EMS calls is the best early indicators for covid-19 ICU needs – does this hold for another region? And does it hold in the same region, but with more recent data?

2. It is unclear why the authors used univariate indicators, rather than combining some indicators into a prediction model which could potentially better predict the outcome.

• Suggestion: consider combining predictors into a prediction model. It is very likely that combining the included indicators would yield the best prediction. Alternatively, discuss why using a single indicator is the best approach here (for ease of use?)

3. Suggestion: please expand the first paragraph on page 7 – in its current form I cannot understand the main portion of the analysis (and Table 1) so it is hard to assess the validity of the general method.

4. As it is currently being described, I don’t think using the indicators to estimate a curve for R(t) is a valid approach, nor do I think it is useful for the overall purpose of this paper.

• Suggestion: I suggest the authors exclude reporting this analysis and focus on the actual prediction of ICU use.

5. Overall suggestion on methods: Reframing the question as a prediction problem and follow standard reporting criteria for reporting prediction models, e.g. https://www.equator-network.org/reporting-guidelines/tripod-statement/

6. The authors mentioned that “massive inter-regional ICU patient transfers” took place to ensure all patients requiring ICU were admitted. However, it is unclear whether this transfer of ICU patients was captured in the data and how the authors had accounted for this. If assuming that the Ile-de-France region had a surplus of ICU patients during the peak of the first wave, that means many patients were transferred to other regions to be treated. Does this mean that the outcome data used in this analysis is an underestimation of ICU patients at the peak of the epidemic?

a. Suggestion: please explain how the transfer of ICU patients influence (or not influence) the interpretation of data in your analysis.

Minor comment:

7. Please put the first paragraph of Results into a table for ease of reading. In particular, it is unclear why the authors chose to report median and IQR for overall measures, and range for COVID-specific measures.

Reviewer #2: The authors presented the results or their investigation describing the response to COVID-19 in some regions of France and tried to find any relationship among the emergency calls and ICU beds occupation. The topic is very interesting and intriguing with the perspective of improving hospital surge capacity response to COVID-19 patients. Unfortunately, the paper needs an extensive english language editing because, in same case, it is very difficult to understand. Following my specific comments

Abstract

1) no data and p values referring to correlation analysis are reported in the abstract.

Paper

Introduction

1) Main aim: in its actual form the main object of the study is not clear. Please rephrase starting from your hypothesis. clarify your hypothesis by pointing out that some indicators ( as the number of telephone calls seems to be) may help in predicting the hospital and ICU surge capacity crisis, for example. This is the pivotal element of the study and, in my opinion, it should be better constructed and argued than actually is.

Methods

2) it seems that the response to an emergency call purely depend on operator judgment. Do you know whether any clinical protocol is employed to manage emergency call? if yes, i think it should be reported in the text.

3) statistical analysis "For each indicator, we determined the onset defined as the first day the indicator became

positive" what does it mean that indicators becomes positive ?

4) "We performed correlation curve analysis during the whole study period by plotting (ICU patients at date T) vs (value of the indicator at date T+t) and varying t, to determine the best correlation coefficient, depending on the number of

days the indicator had been shifted. Please, explain T and t what are referred.

Results

5) "Figure 1 shows the comparison of each indicator to the primary and secondary endpoints". you are referring to the title of a table and you should describe what primary and secondary endpoint mean.

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2020 Nov 18;15(11):e0241406. doi: 10.1371/journal.pone.0241406.r002

Author response to Decision Letter 0

9 Oct 2020

RESPONSE TO THE EDITOR AND REVIEWERS

We thank the Editor and the reviewers for their helpful comments. We would like to offer the following responses to the comments from the Editor and Reviewers and hope that our manuscript will be suitable for publication in PLoS ONE.

EDITOR’S COMMENTS

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Response: We have taken into consideration all indications provided. All changes have been underlined in yellow in the “Manuscript with tracked changes”.

2. We note that in your Ethics Statement you have provided information that your Ethics Approval committee waived the need for informed patient consent, as data were anonymized. Please amend you Methods section to also include this information.

Response: Correction performed in the revised version (Page 5, line 3)

3. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see https://clicktime.symantec.com/3Un5xxLxtXkZvx31VC4hhoA6H2?u=http%3A%2F%2Fjournals.plos.org%2Fplosone%2Fs%2Fdata-availability%23loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see https://clicktime.symantec.com/34KLqYb4t8uy2wmTaXz5Fj46H2?u=http%3A%2F%2Fwww.bmj.com%2Fcontent%2F340%2Fbmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see https://clicktime.symantec.com/3843Rhus5YdvdNf4zA5eu586H2?u=http%3A%2F%2Fjournals.plos.org%2Fplosone%2Fs%2Fdata-availability%23loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

Response: Some of the raw data (number of general practitioner and emergency department visits, percentage of RT-PCR, number of ICU patients, number of hospitalizations) are publicly available (https://www.data.gouv.fr) and this information is clearly provided in the method section. Other raw data not publicly available (EMS calls and ambulance dispatches) are now provided as supplement information (S1 Table). We think that we now follow PLoS ONE policy concerning data availability.

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'No. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.'

At this time, please address the following queries:

a. Please clarify the sources of funding (financial or material support) for your study. List the grants or organizations that supported your study, including funding received from your institution.

b. State what role the funders took in the study. If the funders had no role in your study, please state: “The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”

c. If any authors received a salary from any of your funders, please state which authors and which funders.

d. If you did not receive any funding for this study, please state: “The authors received no specific funding for this work.”

Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

Response: In the revised version, we have added the following statement: “The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. However, one member of the authors’ consortium is the Chief Executive Officer of one of these institutions, namely Martin Hirsch for Assistance Publique-Hôpitaux de Paris.” (Page 20, lines 15-18).

5. Your ethics statement should only appear in the Methods section of your manuscript. If your ethics statement is written in any section besides the Methods, please delete it from any other section.

Response: In the revised version, we have deleted the two sections at the end of the text which were named “Ethics approval and consent to participate” and “Consent for publication”

Response: Supporting information has been provided the end of the manuscript and in-text citations have been corrected.

7. While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Response: We have used the PACE digital diagnostic tool and provided in the revised version only figures that meet PLOS requirements. Thus, we have slightly modified Figure 3 in order it meet this requirements.

REVIEWER #1

1. The correlation between many indicators and the outcomes may not be generalizable to the future (which is the main goal of this paper). For example, the authors used % test positive as one indicator, but % test positive, although an indicator for the spread of SARS-CoV-2 in the community during the early phases of the pandemic (due to testing shortage), in many settings it is starting to become an indicator of testing availability and how widespread testing was, rather than an indicator for community spread. Even if % test positive could indicate ICU capacity needs in the early phases of the pandemic in the Ile-de-France region, its correlation with ICU needs is unlikely to stay the same during a second wave of COVID-19. Similar arguments can be made for other indicators as well, where policy and resource availability changes can potentially change the correlation between an indicator and the outcome.

Response: We thank the Reviewer for this important comment. We have tried to avoid as much as possible the word and concept of “prediction” because we think that our study cannot really predict the evolution of the epidemic itself for many reasons clearly explained to the reader in the introduction when we said that “The peak of the crisis depends indeed on collective measures (testing, isolation of infected patients, social distancing, wearing mask, hand washing, and lockdown), which are the only actions with proven efficacy in the absence of proven specific treatment and/or vaccination to date [9]” (Page 3, lines 13-16). In contrast, we think that our study only provides an anticipated estimation of the number of ICU beds required by analysing variables that are obviously time-linked to it, with different time lag. Concerning the methodology of prediction applied to this problem, we have tried to apply the TRIPOD criteria as much as possible but many of them do not apply to this study, particularly performance reporting, blinding, and use of multivariate analysis (see our response to comment ≠ 2 below). However, the main criticism of the Reviewer is the lack of external validation in our study and we must recognize that this is an important criticism. Unfortunately we were unable to conduct such analyses in other regions of France because only two of them were markedly involved in the epidemic during the first wave and, overall, we should be unable to obtain comparable data for all indicators tested. It should be noted that the computer systems in EMS are very heterogeneous in France and the identification of emergency calls for Covid-19 has not been done in the same reliable way we used. Thus it is not possible for us to provide external “geographical” validation. At the moment of submission of this manuscript (July) we were also unable to assess external validity by using another time frame which did not yet exist. This is clearly not the case now because our region (in fact the whole France as for many other countries) is facing what we think is the onset of a second epidemic wave. Therefore, we have conducted a new analysis during this second period (From August 1 to September 15, 2020) and provide the corresponding qualitative analysis in the revised version which provided similar results. Thus, in the revised version, we have added a sentence in the abstract (Page 2, lines 15-17), in the methods section (Page 7, lines 1-4), and in the discussion (Page 13, lines 21-23) and added new figures as supplement information (Figs S4 and S5). Only a qualitative analysis could be performed since data on primary and secondary endpoints (ICU patients) are obviously truncated during this very early phase and thus anticipation delays could not be calculated. We hope that will alleviate the Reviewer’s criticism since these new results confirm our initial hypothesis.

2. It is unclear why the authors used univariate indicators, rather than combining some indicators into a prediction model which could potentially better predict the outcome.

Response: We did not want to perform a multivariate analysis for several reasons. First, we think that some of these indicators may not be available in some countries or regions, precluding the use of a multivariate indicator elsewhere. Second, an important result of the analysis is the time delay between each indicator and the endpoint (ICU patients). Using a multivariate indicator may provide more robust indications, but will be less timely than the earliest components. In contrast, having several indicators with different time delays may provide a series of successive alerts which reinforce each other. This is really what happened and how we dealt on a daily basis with this panel of indicators just before the occurrence of the second wave. However, we think that the comment of the Reviewer deserve additional information for the reader to explain why a univariate analysis has been preferred. Thus, this point has been added in the revised version (Page 12, lines 15-18). We thank the Reviewer for helping us to better explain that important issue to the reader.

Response: In the revised version, we have tried to better describe the methods used (Page 7, lines 9-10 ). T is clearly defined as the date and t is now defined as the time lag expressed as number of days).

4. As it is currently being described, I don’t think using the indicators to estimate a curve for R(t) is a valid approach, nor do I think it is useful for the overall purpose of this paper.

• Suggestion: I suggest the authors exclude reporting this analysis and focus on the actual prediction of ICU use.

Response: We respectfully disagree with the Reviewer. All indicators included in our study reflect the epidemic curve, obviously to a scaling factor and with a lag. The computation of R(t) therefore provides a valid estimate of the reproduction ratio of the epidemic, as long as the relation between incidence and the indicators remains the same. Here we show that all indicators are likely to reflect the number of Covid-19 cases, as the time-dependant reproduction ratio R(t) computed from these different sources of information yield very commensurate estimates, taking into account the time delay between them. We think that this figure is important to convince epidemiologists that these variables should be considered as variables which appropriately and accurately described the evolution of the epidemic (Page 9, lines 20-21). This point has been added in the revised version to better explain our purpose to the reader (Page 7, lines 11-12).

5. Overall suggestion on methods: Reframing the question as a prediction problem and follow standard reporting criteria for reporting prediction models, e.g. https://clicktime.symantec.com/3GC2PiatM8PM9sHgF7EkmUW6H2?u=https%3A%2F%2Fwww.equator-network.org%2Freporting-guidelines%2Ftripod-statement%2F

Response : As indicated in our response to comment ≠1 above, we wish to avoid the word and concept of prediction in the text of the manuscript. Nevertheless, in the revised version, we have tried to apply the TRIPOD recommendations, at least for the applicable criteria.

a. Suggestion: please explain how the transfer of ICU patients influence (or not influence) the interpretation of data in your analysis.

Response: The Reviewer is correct. In this study, we did not take into account the interregional transfers because they occurred only during the very last period. Moreover, the term “massive” was used because these transfers were unprecedented in France (n=349, including 164 to other countries, i.e. international transfers to Germany, Switzerland, Austria, and Luxembourg but these international transfer did not involve our Ile-de-France region) and because they required considerable human and logistic efforts (fixed wing aircraft, helicopters, and high-speed train) and concerned only critically ill patients requiring mechanical ventilations. However, the number of transferred patients in our region were 252 and represents only 3.9 % of all critically ill patients admitted in ICU. Moreover, concerning the patients transferred to other regions, only part of their length of stay was concerned and since no patient was transferred during the early days after admission into ICU, this issue have no impact on the secondary endpoint (new patients in ICI). In the revised version, to take into account the important comment of the Reviewer, we now clearly indicate the proportion of patients transferred (Page 9, lines 8-10) and discussed this limitation in the discussion (Page 14, lines 14-15 ). In the revised version, we have also indicated that in the method section (Page 5, line 25). We also deleted the term “massive”.

Response: We think that these data are important for the reader to understand the amplitude of variation of each variables and for future comparisons with other regions or countries and thus we wish to maintain them. Nevertheless, we agree that this information may not be included in the main text and that a table might be easier to read. In the revised version, we have provided this information as a supportive information (S1 table, Page 26). We hope that this proposition will satisfy both the Reviewer and the Editor.

REVIEWER #2:

The authors presented the results or their investigation describing the response to COVID-19 in some regions of France and tried to find any relationship among the emergency calls and ICU beds occupation. The topic is very interesting and intriguing with the perspective of improving hospital surge capacity response to COVID-19 patients. Unfortunately, the paper needs an extensive english language editing because, in same case, it is very difficult to understand. Following my specific comments.

1.Abstract: no data and p values referring to correlation analysis are reported in the abstract.

Response: In the revised version, we have indicated the range of coefficient of correlation and the P values (all P<0.001) in the abstract (Page 2, line 13).

2.Paper. Introduction. Main aim: in its actual form the main object of the study is not clear. Please rephrase starting from your hypothesis. clarify your hypothesis by pointing out that some indicators ( as the number of telephone calls seems to be) may help in predicting the hospital and ICU surge capacity crisis, for example. This is the pivotal element of the study and, in my opinion, it should be better constructed and argued than actually is.

Response: We did not see how to better present our introduction which is brief and clearly present the background of the need to anticipate ICU beds, the lack of information from the literature, the aim of the study and the alert signal studied, and lastly why this could be important for health authorities.

3. Methods: it seems that the response to an emergency call purely depend on operator judgment. Do you know whether any clinical protocol is employed to manage emergency call? if yes, I think it should be reported in the text.

Response: Yes, a clinical protocol was employed to manage emergency calls, according to the Ministry of Health recommendations. This information has been added in the revised version (Page 6, lines 11-12) with a citation of the appropriate reference (new reference ≠14).

4. Method. Statistical analysis "For each indicator, we determined the onset defined as the first day the indicator became positive" what does it mean that indicators becomes positive ?

Response: This means that the indicator is greater than zero. This has now been precisely indicated in the revised version (Page 7, line 3).

5. Method. "We performed correlation curve analysis during the whole study period by plotting (ICU patients at date T) vs (value of the indicator at date T+t) and varying t, to determine the best correlation coefficient, depending on the number of

days the indicator had been shifted. Please, explain T and t what are referred.

6. Results: "Figure 1 shows the comparison of each indicator to the primary and secondary endpoints". you are referring to the title of a table and you should describe what primary and secondary endpoint mean.

Response: Correction performed in the revised version (Page 8, lines 9-10).

Other changes performed:

1. Since the preprint cited as a footnote in the text has been accepted for publication, we have included it in the reference list as reference ≠ 33.

2. Because of inclusion of a new reference in the revised version (≠14) all subsequent references have been re-numbered.

3. We changed the title from “Emergency calls are early indicators of intensive care unit bed requirement during the Covid-19 epidemic” to “Early indicators of intensive care unit bed requirement during the Covid-19 epidemic” for two main reasons: a) this title more appropriately reflects the conclusion in the text and in the abstract since it did not focus only on EMS calls; b) we think that this modification is appropriate considering our response to comment ≠2 from Reviewer ≠1.

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PLoS One. doi: 10.1371/journal.pone.0241406.r003

Decision Letter 1

Corstiaan den Uil

15 Oct 2020

Early indicators of intensive care unit bed requirement

during the Covid-19 epidemic

A retrospective study in Ile-de-France region, France

PONE-D-20-23904R1

Dear Dr. RIOU,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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PLoS One. doi: 10.1371/journal.pone.0241406.r004

Acceptance letter

Corstiaan den Uil

19 Oct 2020

PONE-D-20-23904R1

Early indicators of intensive care unit bed requirement during the Covid-19 epidemic A retrospective study in Ile-de-France region, France

Dear Dr. RIOU:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. The Ile-de-France region.

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S2 Fig. Primary endpoint.

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S3 Fig. Secondary endpoint.

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EMS: emergency calls; GP: general practitioner; ED: emergency department; RT-PCR: reverse transcriptase polymerase chain reaction tests.

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Data Availability Statement

Raw data are publicly available (https://www.data.gouv.fr) or provided in the Supporting Information file.

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PERMALINK

Early indicators of intensive care unit bed requirement during the COVID-19 epidemic: A retrospective study in Ile-de-France region, France

Roles

Abstract

Introduction

Material and methods

Statistical analysis

Results

Fig 1. Evolution of the 6 tested indicators compared to the number of intensive care unit (ICU) patients and number of new ICU patients during the study period (from February 20 to May 5, 2020).

Table 1. Observed delays between each indicator and primary or secondary endpoints.

Fig 2. Dashboard of the retained indicators (EMS calls, percentage of positive reverse transcriptase polymerase chain (RT-PCR) tests, and general practitioners (GP) visits) and their respective thresholds and slope had been applied during the initial phase of the epidemic.

Fig 3. Time-dependant reproduction ratio R(t) computed from different sources of information for COVID-19: EMS calls, general practitioner (GP) visits; emergency department (ED) visits, ambulances, and compared to the number of new intensive care unit (ICU) patients.

Discussion

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Corstiaan den Uil

Roles

Author response to Decision Letter 0

Decision Letter 1

Corstiaan den Uil

Roles

Acceptance letter

Corstiaan den Uil

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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PERMALINK

Early indicators of intensive care unit bed requirement during the COVID-19 epidemic: A retrospective study in Ile-de-France region, France

Roles

Abstract

Introduction

Material and methods

Statistical analysis

Results

Fig 1. Evolution of the 6 tested indicators compared to the number of intensive care unit (ICU) patients and number of new ICU patients during the study period (from February 20 to May 5, 2020).

Table 1. Observed delays between each indicator and primary or secondary endpoints.

Fig 2. Dashboard of the retained indicators (EMS calls, percentage of positive reverse transcriptase polymerase chain (RT-PCR) tests, and general practitioners (GP) visits) and their respective thresholds and slope had been applied during the initial phase of the epidemic.

Fig 3. Time-dependant reproduction ratio R(t) computed from different sources of information for COVID-19: EMS calls, general practitioner (GP) visits; emergency department (ED) visits, ambulances, and compared to the number of new intensive care unit (ICU) patients.

Discussion

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Corstiaan den Uil

Roles

Author response to Decision Letter 0

Decision Letter 1

Corstiaan den Uil

Roles

Acceptance letter

Corstiaan den Uil

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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