COVID-19 in-hospital mortality and mode of death in a dynamic and non-restricted tertiary care model in Germany.

BACKGROUND: Reported mortality of hospitalised Coronavirus Disease-2019 (COVID-19) patients varies substantially, particularly in critically ill patients. So far COVID-19 in-hospital mortality and modes of death under state of the art care have not been systematically studied.
METHODS: This retrospective observational monocenter cohort study was performed after implementation of a non-restricted, dynamic tertiary care model at the University Medical Center Freiburg, an experienced acute respiratory distress syndrome (ARDS) and extracorporeal membrane-oxygenation (ECMO) referral center. All hospitalised patients with PCR-confirmed SARS-CoV-2 infection were included. The primary endpoint was in-hospital mortality, secondary endpoints included major complications and modes of death. A multistate analysis and a Cox regression analysis for competing risk models were performed. Modes of death were determined by two independent reviewers.
RESULTS: Between February 25, and May 8, 213 patients were included in the analysis. The median age was 65 years, 129 patients (61%) were male. 70 patients (33%) were admitted to the intensive care unit (ICU), of which 57 patients (81%) received mechanical ventilation and 23 patients (33%) ECMO support. Using multistate methodology, the estimated probability to die within 90 days after COVID-19 onset was 24% in the whole cohort. If the levels of care at time of study entry were accounted for, the probabilities to die were 16% if the patient was initially on a regular ward, 47% if in the intensive care unit (ICU) and 57% if mechanical ventilation was required at study entry. Age ≥65 years and male sex were predictors for in-hospital death. Predominant complications-as judged by two independent reviewers-determining modes of death were multi-organ failure, septic shock and thromboembolic and hemorrhagic complications.
CONCLUSION: In a dynamic care model COVID-19-related in-hospital mortality remained very high. In the absence of potent antiviral agents, strategies to alleviate or prevent the identified complications should be investigated. In this context, multistate analyses enable comparison of models-of-care and treatment strategies and allow estimation and allocation of health care resources.

Introduction

The current SARS-CoV-2 pandemic is a public health emergency of international concern, which poses immense challenges on health care systems [1]. Although modulated by host factors like age and comorbidities, overall about 10–15% of SARS-Cov-2 infected patients require hospitalisation and 20–30% of hospitalised patients develop critical or life-threatening COVID-19 manifestations [2]. Reported mortality rates of COVID-19 patients are in the range of 20–40% [1,3-5] for hospitalised patients and 30–88% for critically-ill or ICU patients with substantial differences between countries and regions [3-10]. Several reasons may account for the observed wide range of these estimates. Referral strategies to the hospital may differ. A high local COVID-19 incidence may put pressure on health care systems leading to restrictions in care with the need to triage patients, and possibly results in high numbers of infected health care workers. Moreover, intensive care unit (ICU) and therefore ventilation and extracorporeal membrane-oxygenation (ECMO) capacities may substantially vary, which may influence admission strategies and decisions on treatment withdrawal.

Compared to neighbouring countries, in Germany the SARS-CoV-2 pandemic started later, providing the health care system and particularly the inpatient sector with valuable time to prepare for a rising case load. The Freiburg University Medical Center, a center with profound expertise in ARDS treatment and ECMO support, formed a Coronavirus task force at the end of January 2020. In the following weeks a COVID-19 dynamic care model was developed and implemented. These preparations together with a relatively high SARS-CoV-2 testing capacity and early lock-down strategies in Germany yielded a situation, in which regional treatment capacities were sufficient at any stage of the pandemic and at any level of care.

We hypothesised that this constitutes a unique opportunity to study the COVID-19-related morbidity and mortality in patients requiring hospitalisation in a setting of non-restricted care. Here we briefly outline the implemented dynamic care model and summarise the corresponding outcomes. Specific aims of the study are i.) to assess COVID-19-related in-hospital mortality in a dynamic and non-restricted care model at an ARDS and ECMO referral center; ii.) to define major complications and modes of death in a setting of extended care with maximum supportive therapy; and iii.) to propagate and stimulate reporting of clinical studies in COVID-19 research using multistate models.

Methods

Study design, setting and participants

The current study constitutes a post hoc analysis of data collected within a retrospective cohort study conducted at the University Medical Center Freiburg. This 1,600-bed tertiary care institution serves the southwest region of the German state of Baden-Württemberg and is one of the largest ARDS and ECMO referral centers in Germany. All hospitalised patients with detection of SARS-CoV-2 using PCR in a respiratory sample between February 25 and May 8, 2020 were eligible and included. The last day of follow-up that was included was June 19.

Beginning in January 2020 the Coronavirus task force at the University Medical Center Freiburg developed a dynamic care model for COVID-19 patients (outlined in ). Patients were treated on COVID-19 regular wards, COVID-19 intermediate care and intensive care units (ICU) run by different departments. Patients were followed during their hospital stay by Infectious Diseases (ID) physicians performing daily COVID-19 rounds. The measures implemented in the COVID-19 response, the evolution of the peak incidences in the region and the corresponding number of admissions in our center are shown in S2 Fig.

Variables collected and definitions

Demographic variables, comorbidities, diagnostic procedures and data on treatment modalities, complications and outcome were extracted by reviewing the admission, transfer and discharge reports and the electronic patient record. Patients were followed until hospital discharge or death.

Comorbidities were recorded in the following eight categories: lung disease (COPD or other chronic pulmonary disease), heart disease (coronary artery disease/ischemic cardiomyopathy or heart failure NYHA II-IV), diabetes mellitus, chronic liver disease (Child B or C), active malignancy, primary or secondary immunodeficiency (the latter being immunosuppressive drugs incl. corticosteroids of ≥20mg/day prednisolone-equivalent), obesity (body mass index [BMI]>30kg/m2) and neurological disease (dementia, stroke or Parkinson’s disease). For Cox regression analysis patients were divided into the groups ‘no comorbidity’ and ‘at least one comorbidity’ present. Hospital-acquired COVID-19 was assumed in the setting of prolonged hospitalisation and if contact tracing yielded contact with other COVID-19 patients or health-care workers in the hospital as the only relevant exposure.

A thorough case review by two independent investigators (intensivists [ICU patients] or ID physicians) concerning complications and modes of death was performed for all patients. All discrepancies between the two reviewers were reviewed and resulted in an additional assessment by a third investigator in order to obtain a final decision.

Classification of ARDS severity was performed according to the Berlin Definition [11]. Indication for ECMO support was in accordance with the guidelines of the Extracorporeal Life Support Organization (ELSO) [12] and did not deviate from usual indications. Multi-organ failure (MOF) was defined as combination of two or more severe organ system dysfunctions. Predominant terminal organ failure during dying process was defined as severe organ dysfunction that either resulted directly in patient´s death or in withdrawal of life support. Concerning the categories ‘Life support in dying process’ and ‘Involvement of COVID-19’, patients were allocated to one category. Reviewers designated each death as either ‚related to COVID-19‘ or ‚unrelated to COVID-19‘.

Ethical consideration

The study and data collection were approved by the Institutional Review Board of the University Medical Center Freiburg (348/20) and was registered in the German Clinical Trials Register (identifier DRKS00021775). We followed the ethical standards set by the Helsinki Declaration of 1964, as revised in 2013, and the research guidelines of the University of Freiburg. The Institutional Review Board of the University Medical Center Freiburg considered the collection of routine data as evaluation of service and waived the need for written informed consent. The Institutional Review Board approved the publication of anonymized data.

Statistical analysis

The primary endpoint was in-hospital mortality. Secondary endpoints included major complications and modes of death. Baseline epidemiological and clinical characteristics, complications and outcomes of patients with and without ICU stay were compared using the t-test or Mann-Whitney-U-test for continuous variables and the χ2 test or Fisher’s exact test for categorical variables.

We performed a Markovian multistate analysis [13] to investigate the mean length of hospitalisation, the mean duration of mechanical ventilation (MV) and ECMO as well as the risks of death and discharge. Multistate model analysis has not only the major advantage that the time dyamics of a patient’s disease progression are taken into account but also that multiple events are studied simultaneously. The model is shown in S3 Fig. The statistical methodology and required assumptions are outlined in detail in [14]. The multistate model accounts for the states hospitalisation in a ‘regular ward’, ‘ICU’, ‘MV’, ‘ECMO’ as well as ‘discharge alive’ and ‘death’. Patients entered the study at the time of hospitalisation due to COVID-19 or at the time of a positive SARS-CoV-2-PCR (in hospital-acquired COVID-19 cases) and were under observation until discharge or death.

For the risk factor analysis, we used a competing risks model to study effects on the time from hospitalisation to death in the hospital. To avoid collider bias, in this model the different states of hospitalisation (regular ward, ICU, MV, ECMO) were not differentiated. First, we estimated cause-specific hazard ratios for death and discharge. These gave information on both direct and indirect effects on the risk of in-hospital death. Then, we estimated the subdistribution hazard ratio of death using a Fine and Gray model. The subdistribution hazard ratio quantifies the effect of risk factors on the absolute risks (rather than the rates) thereby combining the direct and indirect effects found in the cause-specific analysis. Statistical significance was determined at p<0.05. All analyses were performed with R Version 4.0.2.

Results

Epidemiological and clinical characteristics

A total of 213 COVID patients were included in the study (Table 1). The median age was 65 years, 129 patients (61%) were male. Fifty cases (23%) were considered to be hospital-acquired infections. While 56 patients (26%) were without significant comorbidities, 79 patients (37%) reported one, and 78 patients (37%) two or more comorbidities, with coronary artery disease/ischemic cardiomyopathy (21%), diabetes mellitus (20%) and obesity (BMI>30mg/m2, 24%) being the most prevalent diseases. The median time from onset of symptoms to hospitalisation was 6 days. Overall 27 patients (13%) were ICU-referrals from regional hospitals due to complex respiratory or ARDS management and/or the need of ECMO support. During hospitalisation 70 patients (33%) were admitted to the ICU (median SAPS2-score of 46, median Horovitz-index on day 1 of ICU admission 110), of which 57 patients (81%) received invasive MV (median duration 17 days), and 23 patients (33%) needed ECMO support (median duration 11 days, range 1–68 days) (Table 2). Medical treatment included lopinavir/ritonavir (54 patients), hydroxychloroquine (92 patients), and remdesivir (1 patient). Seven patients received tocilizumab. 161 out of 213 patients were discharged alive and 51 patients died. Of the latter, 32 deaths occurred in the ICU (one death after ICU discharge) and 18 deaths on regular wards. At the end of follow-up, one patient, though recovered from COVID-19, was still hospitalised on a regular ward for treatment of an underlying malignancy.

Table 1

Epidemiological and clinical characteristics of 213 COVID-19 patients with and without ICU care.

Parameter	All patients n = 213	Patients with Non-ICU care n = 143	Patients with ICU care n = 70	p-value
Age	65 (54–79;25)	65 (53–80;27)	65 (59–76;17)	0.86 **
Sex male	129 (61)	77 (54)	52 (74)	0.004 *
Time from clinical onset of symptoms to admission (n = 137)	6 (3–9;6)	5 (2–9;7)	7 (4–11;7)	0.04**
NEWS2-Score (n = 172)	7 (3–10; 7)	5 (3–8; 5)	10 (8–12; 4)	<0.0001**
Comorbidities
COPD	13 (6)	6 (4)	7 (10)	0.10*
Coronary artery disease/ischemic cardiomyopathy	45 (21)	29 (20)	16 (23)	0.67*
Malignancy/neoplasm	29 (14)	20 (14)	9 (13)	0.82*
Chemotherapy within last 3 months	9 (4)	6 (4)	3 (4)	0.98*
Primary or secondary immunodeficiency incl. immunosuppressive medication	26 (12)	20 (14)	6 (9)	0.26*
Diabetes mellitus	43/158 (20)	29/92 (20)	14/66 (20)	0.96*
Obesity (BMI >30 kg/m2)	38 (24)	20 (22)	18 (27)	0.42*
Number of comorbid conditions
No comorbid condition	56 (26)	38 (27)	18 (26)	0.95*
1 comorbid condition	79 (37)	52 (36)	27 (39)
≥2 comorbid conditions	78 (37)	53 (37)	25 (36)
Laboratory investigations on admission
Lymphocytes [per μl] (n = 125) Norm: 800–3.000 per μl	830 (510–1170; 660)	870 (560–1170; 610)	710 (470–1110; 640)	0.21**
Thrombocytes [×103/μl] (n = 207) Norm: 176–391 ×103/μl	190 (150–253; 103)	186 (150–235; 85)	217 (150–286; 136)	0.11**
CRP [mg/l] (n = 204) Norm: <5 mg/l	68 (22–134; 112)	36 (12–96; 84)	137 (81–226; 145)	<0.0001**
PCT [ng/ml] (n = 182) Norm: <0,05 ng/ml	0,15 (0,08–0,45; 0,37)	0,11 (0,06–0,19; 0,13)	0,47 (0,21–1,47; 1,26)	<0.0001**
IL-6 [pg/ml] (n = 147) Norm: <7 pg/ml	50 (22–146; 124)	32 (16–51; 35)	175 (77–729; 652)	<0.0001**
D-dimers [mg/l FEU] (n = 97) Norm: <0,5 mg/l	1,4 (0,6–4,6; 4)	1,0 (0,51–1,8; 1,3)	2,3 (1,4–11,9; 10,5)	<0.0001**
Troponin T [ng/l] (n = 127) Norm: <14 ng/l	16 (7–39; 32)	10 (6–30; 24)	29 (12–61; 49)	0.003**
Medical treatment
Intravenous antibiotics	131 (62)	66 (46)	65 (93)	<0.0001*
Lopinavir/ritonavir	54 (25)	17 (12)	37 (53)	<0.0001*
Hydroxychloroquine/chloroquine	92 (43)	39 (27)	53 (76)	<0.0001*
Tocilizumab	7 (3)	1 (1)	6 (9)	0.006***
Outcomes (at end of follow-up)
Discharged, n (%)	161 (69)	124 (87)	37 (53)	<0.0001 *
Death in hospital, n (%)	51 (23)	18 (13)	33 (47)
Still hospitalised, n (%)	1 (0,5)	1 (1)	0 (0)

Data are median and interquartile range (IQR) or numbers (%).

*χ2-test

**Mann-Whitney U test

***Fisher’s exact test.

ICU, intensive care unit; NEWS2, National Early Warning Score 2; BMI, body mass index; COPD, chronic obstructive pulmonary disease; CRP, C-reactive protein; PCT, procalcitonin; IL-6, Interleukin-6; Norm, normal range.

Table 2

Management and complications of 70 ICU patients with COVID-19.

Characteristics of ICU patients	All patients n = 70	Survivors n = 37	Non-Survivors n = 33	p-value
Age	64.5 (59–76)	61 (54–70)	70 (61–78)	0.01**
Direct ICU referrals	27 (39)	16 (43)	11 (33)	0,40*
Blood type 0	15/65 (23)	9/33 (27)	6/32 (19)	0.41*
Blood type A	38/65 (59)	20/33 (61)	18/32 (56)	0.72*
Disease severity upon ICU admission
SAPS2-score (d1)	46 (40–52)	45 (31–50)	49 (45–55)	0,005**
No ARDS or mild ARDS	6 (9)	4 (11)	2 (6)	0,09*
Moderate ARDS	27 (39)	18 (49)	9 (27)
Severe ARDS	37 (53)	15 (41)	22 (67)
Horovitz-Index (lowest in first 24h after ICU admission)	110 (82–126)	114 (88–137)	96 (79–116)	0,13**
ICU Management
High-flow nasal cannula	30 (43)	20 (54)	10 (30)	0,05*
Non-invasive mechanical ventilation	30 (43)	15 (41)	15 (46)	0,68*
High-flow nasal cannula or non-invasive mechanical ventilation (and no invasive mechanical ventilation)	6 (9)	5 (14)	1 (3)	0,20***
Invasive mechanical ventilation	57 (81)	28 (76)	29 (88)	0,23***
Median length of invasive mechanical ventilation, days	17 (8–32)	19.5 (9–40)	15 (7–22)	0,13**
Tracheostomy	26 (37)	17 (46)	9 (27)	0,11*
ECMO	23 (33)	9 (24)	14 (42)	0,11*
Length of ECMO treatment, days	11 (7–21)	9 (8–23)	12 (4–22)	0,79**
ECMO cannulation in external hospital	9/23 (39)	3/9 (33)	6/14 (43)	>0,999***
ECMO weaning successful	12/23 (52)	9/9 (100)	3/14 (21)	0,0003***
Veno-arterial ECMO or left ventricular unloading (Impella®)	4/23 (17)	0	4/14 (29)	0,13***
Prone-positioning	43 (61)	21 (57)	22 (67)	0,40*
Number of prone-positionings per patient	9 (5–13)	8.5 (5–13)	9.0 (6–14)	0,85**
Prone-positioning during ECMO	19/23 (83)	8/9 (89)	11/14 (79)	>0,999***
Repeated neuromuscular blockade	11 (16)	4 (11)	7 (21)	0,33***
Inhaled nitric oxide	6 (9)	4 (11)	2 (6)	0,68***
Complications
Pulmonary embolism (CT-verified)	16 (23)	10 (27)	6 (18)	0,38*
Central pulmonary embolism	5/16 (31)	3/10 (30)	2/6 (33)	>0,999***
Segmental/subsegmental pulmonary embolism	16/16 (100)	10/10 (100)	6/6 (100)	>0,999***
Acute kidney injury with need of renal replacement therapy	26 (37)	12 (32)	14 (42)	0,39*
Replacement of renal replacement system due to thrombosis (at least once)	11/26 (42)	6/12 (42)	5/14 (50)	0,46*
ECMO system or ECMO pump replacement system due to thrombosis (at least once)	12/23 (52)	5/9 (56)	7/14 (50)	>0,999***
Intracerebral bleeding (CT-verified)	11 (16)	5 (11)	6 (16)	0,59*
Intracerebral bleeding w/o ECMO	6/47 (13)	3/28 (11)	3/19 (18)	0,67***
Ischemic stroke	9 (13)	3 (8)	6 (11)	0,29***
Ischemic stroke w/o ECMO	4/47 (9)	2/28 (7)	2/19 (11)	>0,999***
Cardiac arrest with ROSC	6 (9)	1 (3)	5 (15)	0,09***
Pulmonary bleeding	8 (11)	3 (8)	5 (15)	0,46***
Pneumothorax	12 (17)	5 (14)	7 (21)	0,39*
Septic shock	43 (61)	17 (46)	26 (79)	0,005*
Cardiogenic shock	13 (19)	5 (14)	8 (24)	0,25*
Hemorrhagic shock	9 (13)	4 (11)	5 (15)	0,73***
Pulmonary bacterial superinfection†	26 (37)	15 (41)	11 (33)	0,53*
Positive blood cultures	28 (40)	18 (49)	10 (30)	0,12*
Positive blood cultures (without typical contaminants of skin flora)	16 (23)	9 (24)	7 (21)	0,76*
Aspergillus positive respiratory samples with initiation of antifungal therapy	6 (9)	1 (3)	5 (15)	0,09***

Data are median and interquartile range (IQR) or numbers (%).

*χ2-test

**Mann-Whitney U test

***Fisher’s exact test.

ICU, intensive care unit; ARDS, acute respiratory distress syndrome; CT, computed tomography scan; ECMO, extracorporeal membrane-oxygenation.

† Positive respiratory samples with Staphylococcus aureus, Streptococcus pneumoniae or Gram-negative bacteria (Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Proteus mirabilis, Enterobacter cloacae, Citrobacter freundii, Serratia marcescens) with initiation of antibacterial treatment.

Multistate model analysis

Considering all 213 patients in the described dynamic tertiary care model, the population averaged probability to have died 90 days after hospitalisation with COVID-19 was 23.9%. The chance for being discharged alive was 75,6%. There was a 0.5% chance to still be in the hospital after 90 days. A stacked probability plot illustrating the probabilities of COVID-19 patients to be in specific states (regular ward, ICU, MV, ECMO, discharged alive or dead) over the course of time is depicted in Fig 1. Moreover, the plot illustrates the population averaged mean duration spent in each state/level of care. These correspond to the coloured area between two curves.

Fig 1

By accounting for the levels of care when entering the study, i.e. regular ward, ICU, MV, the multistate model allows for an estimation of the approximate length of hospital stay and the probability to be discharged alive or to die at different levels of care. A patient that was first admitted to a regular ward stayed on average 13.6 days in the hospital, 0.8 days in the ICU, 1.4 days with MV and 0.2 days with MV and ECMO within a total stay of 90 days (Fig 2 and S4 Fig). The probability to be discharged alive for patients starting in the ‚regular ward‘-state was 83%, the probability to die was 16%. In contrast, a patient that was admitted to the ICU needed 21.5 days in the ICU, 13.9 days of these with MV and 2.0 days with ECMO. The probability to be discharged alive in the following 90 days was 52%, the probability to die was 47%. Patients that directly required MV stayed 23.6 days on MV, and 8.0 days of these with ECMO. Once MV was no longer required, the patient stayed on average 2.4 more days in the ICU and another 4.0 days on the regular ward. The chances to be discharged alive were only 42%.

Fig 2

Multivariable cause-specific Cox regression analysis

The multivariable regression analysis constitutes a competing risks model with the endpoint in-hospital death and the competing risk discharge alive. According to the cause-specific Cox regression older patients have a higher death hazard (HR 3.45, 95% CI 1.49–7.98, for patients 65–74 years of age, and HR 3.56, 95% CI 1.74–7.30 for ≥75 years-aged patients). Additionally, we found that the discharge hazard is significantly decreased for males (HR 0.68, 95% CI 0.50–0.94) (Table 3). A higher number of comorbid conditions was not significantly associated with altered death or discharge hazards.

Table 3

Multivariable Cox regression analysis.

Model/Analysis	Multivariable Cox regression
Endpoint	Discharge			Death
Variable	Hazard ratio	95% CI	p-value	Hazard ratio	95% CI	p-value
Sex male1	0.68	0.50–0.94	0.020	1.37	0.74–2.54	0.310
Age 65–74 years2	0.63	0.37–1.06	0.079	3.45	1.49–7.98	0.004
Age ≥ 75 years2	0.71	0.49–1.02	0.067	3.56	1.74–7.30	0.001
Hospital-acquired COVID-193	0.73	0.48–1.12	0.155	0.91	0.45–1.84	0.790
Comorbidities present (≥1)4	0.87	0.60–1.25	0.442	1.30	0.61–2.79	0.494
Length of stay5	1.00	0.99–1.01	0.739	0.98	0.94–1.02	0.372
Model/Analysis	Fine and Gray model
Endpoint				Death
Variable				Subdistribution hazard ratio	95% CI	p-value
Sex male1				1.90	1.04–3.48	0.03
Age 65–74 years2				4.16	1.82–9.49	<0.001
Age ≥ 75 years2				4.13	2.05–8.32	<0.001
Hospital-acquired COVID-193				1.18	0.60–2.34	0.59
Comorbidities present (≥1)4				1.25	0.59–2.68	0.55
Length of stay5				0.98	0.94–1.03	0.23

1 Reference: female

2 reference: age 0–64 years

3 reference: community-acquired COVID-19

4 reference: no comorbid condition

5 reference: 0 days (Previous length of stay was the time from hospital admission to COVID-19 onset, for patients with community acquired COVID-19, the length of stay was 0 days).

In the Fine and Gray model yielding subdistribution hazard ratios, the probability to die was significantly increased for males (HR 1.90, 95% CI 1.04–3.48) and patients aged 65 years or older (HR 4.16, 95% CI 1.82–9.49 for age group 65–74 years, and HR 4.13, 95% CI 2.05–8.32 for ≥75 years of age). For males the decreased discharge hazard leads to a prolonged length of stay and therefore increased the risk of death in the hospital. The increased death risk for patients older than 65 is explained by a direct effect on the death hazard. Stacked probability plots (S5–S8 Figs and S1 Data) stratified respectively by age, sex, the presence of comorbidities, immunodeficiency and malignancy/neoplasm illustrate in detail the effect of these risk factors not only on mortality, but also on the six states of the multistate model.

Complications and presumed modes of death

According to the individual case review, ICU patients (both, survivors and non-survivors) suffered from a multitude of complications (Table 2), the four dominant ones being septic shock in 43 patients (61%), acute kidney injury with the need for renal replacement therapy in 26 of 70 patients (37%), as well as thromboembolic and hemorrhagic complications. Pulmonary embolism was diagnosed in 16 patients (23%). Replacement of extracorporeal devices due to thrombosis had to be performed in 11 of 26 patients (42%) on renal replacement therapy and 12 of 23 patients (52%) on ECMO. Ischemic stroke occurred in 9 of 70 patients (13%). Major hemorrhagic manifestations were intracerebral bleeding in 11 patients (16%) and pulmonary hemorrhage in 8 patients (11%).

As of June 19, 2020, 18 patients died on regular wards. The median age of these patients was 80 years–in accordance to the patients’ will, ICU transfer/treatment and MV was withheld in these patients. Death was due to respiratory failure in 12 patients and multi-organ failure in 6 patients.

All but four patients that received ICU care succumbed due to multi-organ failure (Tables 2, 4 and 5). A median of three organ systems were involved with lung failure (32 patients), kidney/renal failure (24 patients), brain injury (17 patients), heart failure (14 patients) and gastrointestinal injury (13 patients, in particular acute mesenteric ischemia) being the predominant terminal organ failures involved. In 21 of 33 patients (63%) septic shock was a critical complication considered to be relevant for multi-organ failure and death. Of 51 patients that died, death was presumed to be secondary to COVID-19 in 30 patients with frailty/comorbidities. Sixteen patients (31%) without relevant comorbidities, i.e. without underlying diseases impacting on life expectancy, died due to COVID-19 or COVID-19-related complications.

Table 4

Critical terminal organ failure and modes of death in 51 patients with COVID-19.

Parameter	Patients who died n = 51	Patients who died Non-ICU care n = 18	Patients who died ICU care n = 33	p-value*
Predominant terminal organ failure during dying process
Septic shock	21 (41)	0	21 (63)	0.001
Multiorgan failure (n> = 2)	35 (69)	6 (33)	29 (88)	0.001
Failure of 2 organs	9 (18)	4 (22)	5 (15)	0.03**
Failure of 3–4 organs	16 (31)	2 (11)	14 (42)
Failure of >4 organs	10 (20)	0	10 (30)
Lung failure	49 (96)	17 (94)	32 (97)	>0,999
IMV and ECMO used	14 (28)	0	14 (42)	<0.0001
IMV used, no ECMO used	16 (31)	1 (6)	15 (46)
No IMV, no ECMO used	19 (37)	16 (89)	3 (9)
Heart failure	15 (29)	1 (6)	14 (42)	0.009
Kidney injury	27 (53)	3 (17)	24 (73)	0.0003
Gastro-intestinal injury	13 (26)	0	13 (39)	0.002
Liver failure	9 (18)	1 (6)	8 (24)	0.13
Brain injury any	20 (39)	3 (17)	17 (52)	0.02
Intracerebral hemorrhage	5 (10)	0	5 (16)	0.15
Thrombembolic event and non-cerebral hemorrhage	11 (22)	0	11 (33)	0.005
Cardiogenic shock	7 (14)	0	7 (21)	0.04
Cardiac arrest—CPR w/o ROSC	5 (10)	1 (6)	4 (12)	0.64
Life support in dying process
Withholding of ICU	17 (33)	17 (94)	0	<0.0001**
Initial ICU therapy, withdrawal in worsening condition	18 (35)	0	18 (55)
Full care	16 (31)	1 (6)	15 (46)
Involvement of COVID-19 as jugdeg by two independent reviwers
Death presumed due to COVID-19 in patients with normal life expectancy	16 (31)†	1 (6)	15 (46)	0.01**
Death presumed due to COVID-19 in patient with frailty/comorbidities	30 (59)	15 (83)	15 (46)
Death presumed due other condition incl. frailty/comorbidities	5 (10)	2 (11)	3 (9)

Data are numbers (%).

*Fisher’s exact test, except

**χ2-test.

ECMO, extracorporeal membrane-oxygenation; IMV, invasive mechanical ventilation; CPR w/o ROSC, cardiopulmonary resuscitation without return of spontaneous circulation.

† Mean years of potential life lost (YPLL) per patient (according to current average life expectancy) 13,1 years.

Table 5

Terminal organ failure and modes of death in 51 patients with COVID-19 as judged by two independent reviewers.

Predominant terminal organ failure during dying process	Lung failure and ECMO support	Lung failure and invasive MV (w/o ECMO)	Lung failure (w/o ECMO or MV)	heart failure	Kidney injury	Gastro intestinal failure
Discordance	0	3	0	10	4	5
Concordance	0	48	51	41	47	46
% concordance after second review	100	94	100	80	92	90
Predominant terminal organ failure during dying process	Liver failure	Brain injury	Thrombembolic event and non-cerebral hemorrhage	Septic shock	Cardiogenic shock	CPR w/o ROSC
Discordance	6	5	3	6	4	3
Concordance	45	46	48	45	47	48
% concordance after second review	88	90	94	88	92	94
	Life support in dying process			Involvement of COVID-19 as jugdeg by two independent reviewers
	Withholding of ICU	Withdrawal of ICU therapy	Full care	Death presumed to COVID-19 in patients with normal life expectancy	Death presumed due to COVID-19 in patient with frailty/comorbidities	Death presumed due other condition incl. frailty/ comorbidities
Discordance	0	17	11	7	14	8
Concordance	51	34	40	44	37	43
% concordance after second review	100	67	78	86	73	84

Discussion

The principal findings of this study are as follows. i.) In the implemented care model yielding non-restricted conditions at an experienced ARDS and ECMO referral center, COVID-19-related in-hospital-mortality remained high at around 25%. ii.) Older age and male sex were independent risk factors for death. iii) In patients requiring ICU care, 1 out of 2 patients died with critical events being lung and multi-organ failure, septic shock, and thromboembolic and hemorrhagic complications. iv.) In the setting of a referral center the average length of stay in the hospital for COVID-19 patients was 16 days if admittance was to a regular ward, 26.5 days for patients admitted to the ICU, and 30 days in the case of initial MV in hospital. In the latter group 11 days of ECMO support were required.

In the ongoing SARS-CoV-2 pandemic solid estimates on patient outcomes such as mortality and major complications are pivotal and strongly required by medical and social institutions, yet difficult to generate [15]. Although COVID-19 studies are published at unprecedented frequency and speed, comparability of studies is hampered by the use of different study designs, varying standards of reporting and the statistical approaches used. So far, the majority of studies, particularly those in critically-ill or ICU patients, reported on preliminary in-hospital mortality rates, as 23–72% of patients were still hospitalised at the time of reporting [5,7-10].

We believe our study provides superior estimates on mortality, complications and length of stay, as different study set up and analytical approaches compared to previous studies were employed. First, by implementing a dynamic care model, we excluded that the need to triage patients, or the availability of limited ICU capacities impacted on mortality rate in a major way. Moreover, given the experience of a large interdisciplinary ARDS and ECMO referral center together with a highly active ID service, the conditions to manage critically ill COVID-19 patients with severe pneumonia and development of ARDS adhered to highest international standards. However, the COVID-19 related in-hospital mortality rate of 24% overall, of 47% in the ICU subgroup and of 57% in the MV subgroup remained substantial even under maximal respiratory support with prolonged provision of ECMO and other advanced therapies including prone-positioning. Of note, about one third of patients that died were without relevant comorbidities and were believed to have a normal life expectancy prior to SARS-CoV-2 infection.

The identified risk factors for death, namely age and male sex, are in line with findings of published studies. Interestingly, application of a competing risk model identified male sex to be associated with a decreased discharge hazard, thereby contributing indirectly to an increased risk of death. Comorbidities were either equally distributed or more often prevalent in the ICU subgroup, with the only exception of immunodeficiency, which was more frequent in the Non-ICU group. Although not adjusted to other factors, our results point towards a comparable COVID-19-related mortality in patients with and without immunodeficiency.

The present study comprises 70 ICU patients, including 23 patients with ECMO support. It is the first study with a completed follow-up, as all patients were discharged from the ICU. The only patient still in hospital has recovered from COVID-19. Importantly, our study provides detailed information on complications and presumed modes of death. This detailed analysis reveals that in the course of prolonged respiratory support a range of serious and outcome-relevant complications arise. The observed pattern with multi-organ failure implicates that COVID-19, at least in critically ill patients, should be regarded as a multi-system disease that reaches far beyond the respiratory tract and severe ARDS. This is in line with recent reports on endothelial cell involvement and diffuse vascular organ changes [16,17]. Further investigations including histopathological analysis of organ biopsies (ante- and post-mortem) are needed to elucidate critical organ involvement, as well as underlying pathophysiological mechanisms. The high rate of thromboembolic complications corroborates recent findings in case series and autopsy studies of a pronounced coagulopathy in severe COVID-19 [18-21]. The observed high incidence of septic shock possibly contributed to a compromised microcirculation, but may also be a consequence thereof. However, given the severity of COVID-19 in the ICU subgroup (indicated by the high proportion of moderate and severe ARDS, low Horovitz indices and the high rate of complications) it is noteworthy that 1 out of 2 ICU patients was discharged alive.

In the context of COVID-19, randomised controlled trials cannot be realised for all treatment modalities (pharmacological or supportive). Therefore data of observational studies will need to be analysed and compared [22,23]. In the current study we take advantage of a multistate model analysis [13]. This approach provides insights into time-dynamic effects and clinical outcomes, avoids common survival biases, and acknowledges active cases by taking into account censoring. In addition to the predicted probabilities for discharge and death, expected average durations in hospital can be calculated for the different states [24]. Visualisation using a stacked probability plot provides easy-to-interpret, yet compact and comprehensive information on the patients’ clinical progress. This is in line with the proposals of the WHO and the COMET initiative regarding endpoints in clinical COVID-19 studies [25]. By applying such a multistate analysis our study provides firm estimates of in-hospital mortality rates and allows a more precise calculation of required ICU and ECMO capacities and therefore allocation of resources in a given care model [26].

Our study has limitations, primarily those inherent to its retrospective observational design. It is a monocenter study, which may limit generalisability. Yet the monocentric design may be considered a prerequisite to study treatment results in a specific care model at an experienced ARDS center. The limited number of patients precluded an analysis of specific treatment strategies, both in terms of antiviral or anti-inflammatory agents, anticoagulation strategies, and time-sensitive supportive strategies. While the primary endpoint of in-hospital death is reliably determined retrospectively, uncertainties remain in evaluating the mode of death. We tried to minimize this uncertainty by performing individual case review by two independent experienced physicians and explicitly avoiding causal assumptions.

Conclusions

In summary, our study delineates that even under non-restricted care conditions COVID-19-related morbidity and mortality is high, especially in patients needing ICU management. Beside the search for potent antiviral agents, future research efforts should focus on strategies to alleviate or prevent complications identified in our study. Moreover, our findings underline the need for continued efforts in preventive measures and development of an effective vaccine. Finally, we demonstrate that by using a multistate model solid estimates for required ICU and ECMO capacities can be provided. Therefore, this work exemplifies, how best to report on COVID-19 studies to allow for meaningful comparisons of different treatment and care modalities.

COVID dynamic care model of the University Medical Center Freiburg.

Patient flow in the dynamic care model established by the Task force Coronavirus (consisting of representatives of the ID department, Emergency department, Virology and Infection control Departments and the Pandemic Operational Committee of the University Medical Center Freiburg): Patients from the outpatient setting or inter-hospital tranfers were evaluated in dedicated areas in the emergency department. Confirmed COVID patients were distributed according to severity of disease on regular wards with or without monitoring. Patients with suspicion of COVID were admitted to separate holding areas. Unstable patients, ICU transfers or admissions to the ECMO facility were managed via the ICU coordinator and allocated to dedicated ICU and ECMO facilities. The dynamic care model included an escalation strategy, in which additional regular wards and ICU beds were equipped, physicians and nursing staff were trained and these wards were subsequently recruited upon utilisation of a certain threshold of COVID bed capacities. ID Infectious diseases, ICU Intensive care unit, IMC Intermediate care ward, COVID Coronavirus Disease 2019.

(PNG)

Click here for additional data file.

COVID response at the University Medical Center Freiburg.

The measures implemented in the COVID-19 response, the evolution of the peak incidences in the region (COVID-19 cases/100.00/day [dates of registration at local health authorities]) and the corresponding number of admissions in the University Medical Center Freiburg.

(PNG)

Click here for additional data file.

Schematic diagram of the applied multistate model.

The six state model considers the events hospitalisation in 1) regular ward, 2) ICU, 3) mechanical ventilation (MV), 4) ECMO, 5) discharge and 6) death. The boxes represent the possible states a patient may encounter and the arrows represent the possible transitions from one state to another. Thus, the arrows between the states show which transitions are possible.

(PNG)

Click here for additional data file.

Stacked probability plots for the multistate model stratified by age.

Stacked probability plots for the multistate model stratified by age. The plots illustrate in more detail the results of the competing risks regression models (however, not adjusted for other covariates). The graphs indicates that older patients have an increased risk to stay longer in hospital, to be admitted to the ICU, to need mechanical ventilation (including for a longer duration), and to die in hospital.

(PNG)

Click here for additional data file.

Stacked probability plots for the multistate model stratified by sex.

Stacked probability plots for the multistate model stratified by sex. The plots illustrate in more detail the results of the competing risks regression models (however, not adjusted for other covariates). The graphs indicates that male patients have an increased risk to be admitted to the ICU, to need mechanical ventilation, and to die in hospital.

(PNG)

Click here for additional data file.

Stacked probability plots for the multistate model stratified by the presence of comorbidities.

Stacked probability plots for the multistate model stratified by the presence of comorbidities. The plots illustrate in more detail the results of the competing risks regression models (however, not adjusted for other covariates). The graphs indicates that patients with one or more comorbidities have an increased risk to stay longer in hospital, to be admitted to the ICU, to need mechanical ventilation, and to die in hospital.

(PNG)

Click here for additional data file.

Stacked probability plots for the multistate model stratified by the presence of immunodeficiency.

Stacked probability plots for the multistate model stratified by the presence of immunodeficiency. The plots illustrate in more detail the results of the competing risks regression models (however, not adjusted for other covariates). The graphs indicates that immunodeficient patients have an increased risk to stay longer in hospital, yet, a decreased risk to be admitted to the ICU, to need mechanical ventilation, and to die in hospital.

(PNG)

Click here for additional data file.

Stacked probability plots for the multistate model stratified by presence of malignancy/neoplasm.

Stacked probability plots for the multistate model stratified by presence of malignancy/neoplasm. The plots illustrate in more detail the results of the competing risks regression models (however, not adjusted for other covariates). The graphs indicates that patients with malignancies or neoplasms have a slightly increased risk to be admitted to the ICU and to need mechanical ventilation, yet, no increased risk to die in hospital.

(PNG)

Click here for additional data file.

R-code for data analysis.

(HTML)

Click here for additional data file.

Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

18 Sep 2020

PONE-D-20-25207

COVID-19 in-hospital mortality and mode of death in a dynamic and non-restricted tertiary care model in Germany

PLOS ONE

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Additional Editor Comments:

Thank you very much for your contribution. The paper is of great interest but it needs some minor issues to be answered.

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Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: No

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Reviewer #1: Review of the article entitled “COVID-19 in hospital mortality and mode of death…”

The Article addresses a clue topic nowadays. It has the pride to be well designed and to be methodologically appropriate for the aims. The format is clinical and observational retrospective. Although done in a single hospital, the study population is large with the advantage to use an already well entrained network of expertise. This special point is of value when the impact of comorbidities on the survival rate is addressed as well as when the cause of fatal events is assessed.

Analytical revision

Abstract

Line 3: “optimized care conditions”. I suggest “state of the art care”. Needless to say, the search for stratification of the clinical severity and of the treatment are ongoing.

Line 23: ”substantial”. I suggest “very high”.

Text

88-97: The concept of “dynamic care model” in a well-equipped Hospital, that permits to assign patients without restriction, should be more briefly summarized.

115. ”(outlined in Supplementary Figure S 1)”. The Figure S 1 is unessential to address the three aims of the study

Line 118-119: “or being involved via the ID consultation service”. Unessential

119-121: The Supplementary Figure S 2 is interesting in speculative terms since adds information on the real word. It is unessential to address the aims of the study.

140. “Supplementary Table1”. The information given by the Suppl. Table 1 is essential to the value of the study. My suggestion is to include it straightforward in the results section rather than in the method section.

148. “designated each death to, related to COVID-!9 or unrelated to…”. Sentence to be reshaped .

162-163. “The model is shown in Supplementary Figure 3 “. Figure Suppl.3 is unessential.

167-169. both sentences are unessential in the “statistical analysis “paragraph.

209-210. “Supplementary Figure S 4”, although the time span of in hospital stay is of importance, the Figure is complex and similar information may be deducted from the remaining Figures.

229-230. “Supplementary Figures S5-S9”. These figures give great value to the overall work. At a glance the reader can appreciate 1) the prognosis over time according to the class of entry and 2) the chance to switch into a different state. As such I recommend to insert those Figures in the final text. Only the Suppl. Figure S6 does not seem essential, so it can be removed in order to reduce the load. I recommend to rephrase completely the legends by putting in clear the clinical and prognostic meaning beyond the statistical technicality. For instance, the Suppl. Figure 7 could be usefully inserted at line 222-223.

255. ”time points of death are depicted in the cohort plot in Supplementary Figure S4”: as already told , the Figure S4 is overcrowded . It can be removed.

267. “, and on the required health service resources,”: the specification breaks the main message. It could be deleted.

270-272. All the sentence may be rephrased by saying how the information on mortality in ICU patients is strongly required by medical and social institutions.

286: “usage of”: look to a better form.

288-289. The information about the relevance of comorbidities on mortality is essential to the topic. The results of the study are clearly at a difference with the current narration. This point deserves adequate.

The Figure S 8 should be quoted.

303-306. “however…..alive”: the message is clear, although the sentence needs revision.

319-326. “monocenter study”: I do not see a limitation, since homogeneity of decision making is of value in such a complex context. “generalisability” is not mandatory today. ”antiviral or anti-inflammatory agents”: 1. Also anticoagulation is dependent upon clinical sense or imposed by cannulation of the vessels. Not to say the challenge between intravascular thrombosis and life-threatening bleeding.

2. as such a brief comment on the uncertainity of starting anticoagulation could be of help to the reader. In this paragraph or previously.

327. Conclusions: the paragraph sound more as sum up than true assertive conclusions. Since the matter is hot and the study is rich of information, a new draft is recommended.

437.Table 1. “NEWS2 score”: the acronym should be put in extenso in line 439.

“CRP”: same comment. The range of normality has to be added to the first column “PCT”: same comment. The range of normality has to be added to the first column “IL-6”: same comment. The range of normality has to be added to the first column.

The range of normality for D-dimers has also to be added.

Since the occurrence of intravascular thrombosis and of life-threatening bleeding were so high in the study population, at least the count of the platelets should be included in order to infer potential consumption.

Medical treatment: Heparin and Corticosteroids should be included in the list, if employed.

460. Table 4: “Cardiac arrest – CPR w/o ROSC”: the acronyms should be put in extenso at the bottom. Infact cardiac arrest is clinically clear and further specifications could be unessential.

478-479: Figure 2. The numerosity of each group should be written as (n=….). It is very needed since the MV group is obviously included in the ICU group.

Reviewer #2: COVID-19 in-hospital mortality and mode of death in a dynamic and non-restricted tertiary care model in Germany

The authors provide a retrospective study of COVID-19 patients care at the University Medical Center Freiburg during the peak of the epidemic in March/April 2020. This is an important study for the research community as much can be learned from the experience of medical doctors and scientists that were in direct contact with patients, in terms of survival rates, treatment and hospital management. Additional factors that make this study interesting are that the Medical Center had at its disposal the latest available treatments and knowledge, and had enough space in its ICU unit so that it was not overwhelmed by the number of patients in critical conditions.

The authors use a multistate analysis, where a patient moves through different states before being released healthy or passing away, which allows them to understand how different categories of patients (elderly vs relatively younger, male vs female) are likely to progress once they enter the Medical Center. They also use a survival analysis to demonstrate that elderly and male patients are more likely to have complications.

Overall, the paper is very well written, however we think that improvements are necessary before we can recommend it for publication.

Most importantly, more explanation of the methodology used is necessary to ensure the clarity and reproducibility of the results, especially because this article is likely to reach a wide audience that is not restricted to experts in survival analysis. Also it seems that only summary data are provided in tables, which it seems not to be enough to comply with the full data availability statement.

Major points:

• In methods, it is mentioned that a multistate analysis is used, but no reference is given, nor details of what mathematical model was used and how it was implemented. Was the multistate model a Markov Chain? What are the assumptions here? How were the state change rates estimated?

• It is also mentioned that a risk factor analysis (competing risk model) is used, where the different states of hospitalisation were not differentiated, but also in this case no much explanation is given. Are modelling assumptions met? (e.g. proportionality of hazards). If a multistate survival analysis was used already (where basically each transition rate is a survival model), why use a different survival model here, where states are not considered?

• Fine and Gray model was used in the results but not discussed in the methods. Please provide more information about this method, why it was employed and what assumptions it required.

• In general, I would like to see more details of methods, such as what mathematical models were used, what assumptions, if the assumptions were met, citing appropriate literature, explaining why these method and not other competing methods were used.

• Another major point missing is whether there was anything that could be learned from treatments? How did they affect the path to discharge or death of the patients? Were they taken into consideration as confounding variables of the models? It would be useful to add at least a remark that this was investigated, even if the results of the effect of treatment were unconclusive.

• Code should be available for reproducibility for example by upload to a github account. At line 177, Rstudio version is not informative, it is just an advanced text editor for R code, authors should report which version of R was used and which R packages.

• I believe all data points should be available, not only summary data. The authors stated that all data were available but I was only able to find summary data. Could you provide a text file on github or an excel file as supplementary data?

• Is it possible to provide or estimate the false positive rate of the PCR-based test used to determine the SARS-CoV-2 infection? It could be interesting/relevant to know.

Minor points:

• Line 49, please spell out ARDS and ECMO in the abstract and put the abbreviation in parenthesis. Please do this for all abbreviation when used the first time.

• Line 86 I would remove “e.g.” as it seems unnecessary

• Line 305 “1 out of two ICU patients could was discharged alive”, remove “could” or change “could was” into “could be”.

• Line 307, please add a comma after “COVID-19”

**********

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Reviewer #1: No

Reviewer #2: No

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26 Oct 2020

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

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Reply: We tried to fulfill all format requirements.

2. 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.

Reply: Was corrected.

Additional Editor Comments:

Thank you very much for your contribution. The paper is of great interest but it needs some minor issues to be answered.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

Due to the German Federal Data Protection Act (Bundesdatenschutzgesetz) and the fact that the Institutional Review Board of the University Medical Center Freiburg granted publication of only anonymised data, inclusion of the complete dataset is not possible. We included a statement in the Ethical Consideration section (see also in reply to Reviewer #2).

________________________________________

4. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

5. Review Comments to the Author

Reviewer #1: Review of the article entitled “COVID-19 in hospital mortality and mode of death…”

The Article addresses a clue topic nowadays. It has the pride to be well designed and to be methodologically appropriate for the aims. The format is clinical and observational retrospective. Although done in a single hospital, the study population is large with the advantage to use an already well entrained network of expertise. This special point is of value when the impact of comorbidities on the survival rate is addressed as well as when the cause of fatal events is assessed.

Analytical revision

Abstract

Line 3: “optimized care conditions”. I suggest “state of the art care”. Needless to say, the search for stratification of the clinical severity and of the treatment are ongoing.

Line 23: ”substantial”. I suggest “very high”.

Reply: Both corrected.

Text

Lines 88-97: The concept of “dynamic care model” in a well-equipped Hospital, that permits to assign patients without restriction, should be more briefly summarized.

Reply: We shortened the respective paragraph.

Line 115. ”(outlined in Supplementary Figure S 1)”. The Figure S 1 is unessential to address the three aims of the study.

Reply: We agree that the graph is not essential with regard to answering the primary questions addressed – that’s why we decided to move the figure into the supplementary material. However, as the organisational matters are of interest to other institutions (we already received several comments/requests) we would prefer to keep the Figures S1 and S2 included in the supplementary material.

Line 118-119: “or being involved via the ID consultation service”. Unessential

Reply: Deleted.

119-121: The Supplementary Figure S 2 is interesting in speculative terms since adds information on the real word. It is unessential to address the aims of the study.

Reply: see comment above (line 115).

140. “Supplementary Table1”. The information given by the Suppl. Table 1 is essential to the value of the study. My suggestion is to include it straightforward in the results section rather than in the method section.

Reply: As suggested we included the former Suppl. Table 1 now as Table 5 in the main manuscript.

148. “designated each death to, related to COVID-19 or unrelated to…”. Sentence to be reshaped .

Reply: Done.

162-163. “The model is shown in Supplementary Figure 3 “. Figure Suppl.3 is unessential.

Reply: For those that are not abreast of multistate analyses, the figure is of great help in illustrating the possible transitions between the different states. We therefore would like to keep Supplementary Figure 3 included in the supplementary material.

167-169. both sentences are unessential in the “statistical analysis “paragraph.

Reply: Both sentences were deleted.

209-210. “Supplementary Figure S 4”, although the time span of in hospital stay is of importance, the Figure is complex and similar information may be deducted from the remaining Figures.

Reply: Supplementary Figure S 4 was deleted.

229-230. “Supplementary Figures S5-S9”. These figures give great value to the overall work. At a glance the reader can appreciate 1) the prognosis over time according to the class of entry and 2) the chance to switch into a different state. As such I recommend to insert those Figures in the final text. Only the Suppl. Figure S6 does not seem essential, so it can be removed in order to reduce the load. I recommend to rephrase completely the legends by putting in clear the clinical and prognostic meaning beyond the statistical technicality. For instance, the Suppl. Figure 7 could be usefully inserted at line 222-223.

Reply: Beside the strongest predcitor age, sex is a consistently found risk factor for a worse outcome - we do not see why this graph is less informative than the others. We agree that the stacked probability plots are very instructive, however, we are concerned that there will be too many figures in the main manuscript, if we move Figures S5-S9 in the final text. We would of course be happy to follow Editorial guidance on this.

As suggested we modified the figure legends and now delineate the clinical and prognostic meaning of the stacked probability plots.

255. ”time points of death are depicted in the cohort plot in Supplementary Figure S4”: as already told , the Figure S4 is overcrowded . It can be removed.

Reply: Supplementary Figure S 4 was deleted.

267. “, and on the required health service resources,”: the specification breaks the main message. It could be deleted.

Reply: Done.

270-272. All the sentence may be rephrased by saying how the information on mortality in ICU patients is strongly required by medical and social institutions.

Reply: We restructured the sentence and included the information.

286: “usage of”: look to a better form.

Reply: Was replaced by ‚application of‘.

288-289. The information about the relevance of comorbidities on mortality is essential to the topic. The results of the study are clearly at a difference with the current narration. This point deserves adequate. The Figure S 8 should be quoted.

Reply: We included a specific remark on the impact of immunodefciency in the discussion (lines 340-341 in track change modus version).

303-306. “however…..alive”: the message is clear, although the sentence needs revision.

Reply: The mistake was corrected.

319-326. “monocenter study”: I do not see a limitation, since homogeneity of decision making is of value in such a complex context. “generalisability” is not mandatory today. ”antiviral or anti-inflammatory agents”: 1. Also anticoagulation is dependent upon clinical sense or imposed by cannulation of the vessels. Not to say the challenge between intravascular thrombosis and life-threatening bleeding. 2. as such a brief comment on the uncertainity of starting anticoagulation could be of help to the reader. In this paragraph or previously.

Reply: We agree with this notion and already point out, that the monocenter design is needed if specific levels or models of care are studied. Moreover, we included anticoagulation as a potential but so far undefined management strategy (line 375 in track change modus version).

327. Conclusions: the paragraph sound more as sum up than true assertive conclusions. Since the matter is hot and the study is rich of information, a new draft is recommended.

Reply: We thank the reviewer for this comment and modified the conclusions accordingly. We emphasise now on the consequences and future research needs that arise from the results of our study.

437. Table 1. “NEWS2 score”: the acronym should be put in extenso in line 439.

“CRP”: same comment. The range of normality has to be added to the first column “PCT”: same comment. The range of normality has to be added to the first column “IL-6”: same comment. The range of normality has to be added to the first column. The range of normality for D-dimers has also to be added.

Reply: The requested information was added.

Since the occurrence of intravascular thrombosis and of life-threatening bleeding were so high in the study population, at least the count of the platelets should be included in order to infer potential consumption.

Reply: The requested information was added.

Medical treatment: Heparin and Corticosteroids should be included in the list, if employed.

Reply: We here report patients from the first 2,5 months of the pandemic. Data from trials investigating the impact of corticosteroids (e.g. RECOVERY trial) were not available yet, thus, no patient received corticosteroids with COVID-19/COVID-19-associated ARDS as indication. Concerning heparin, all hospitalised patients received low-molecular weight (or heparin if the former was contraindicated) in the usual prophylaxis dose. Those patients with confirmed thrombembolic events received therapeutic anticoagulation, however, there was no systematic use of an anticoagulation strategy.

460. Table 4: “Cardiac arrest – CPR w/o ROSC”: the acronyms should be put in extenso at the bottom. Infact cardiac arrest is clinically clear and further specifications could be unessential.

Reply: The requested information was added.

478-479: Figure 2. The numerosity of each group should be written as (n=….). It is very needed since the MV group is obviously included in the ICU group.

Reply: The requested information was added in Figure 2.

Reviewer #2: COVID-19 in-hospital mortality and mode of death in a dynamic and non-restricted tertiary care model in Germany

The authors provide a retrospective study of COVID-19 patients care at the University Medical Center Freiburg during the peak of the epidemic in March/April 2020. This is an important study for the research community as much can be learned from the experience of medical doctors and scientists that were in direct contact with patients, in terms of survival rates, treatment and hospital management. Additional factors that make this study interesting are that the Medical Center had at its disposal the latest available treatments and knowledge, and had enough space in its ICU unit so that it was not overwhelmed by the number of patients in critical conditions.

The authors use a multistate analysis, where a patient moves through different states before being released healthy or passing away, which allows them to understand how different categories of patients (elderly vs relatively younger, male vs female) are likely to progress once they enter the Medical Center. They also use a survival analysis to demonstrate that elderly and male patients are more likely to have complications.

Overall, the paper is very well written, however we think that improvements are necessary before we can recommend it for publication.

Most importantly, more explanation of the methodology used is necessary to ensure the clarity and reproducibility of the results, especially because this article is likely to reach a wide audience that is not restricted to experts in survival analysis. Also it seems that only summary data are provided in tables, which it seems not to be enough to comply with the full data availability statement.

Major points:

• In methods, it is mentioned that a multistate analysis is used, but no reference is given, nor details of what mathematical model was used and how it was implemented. Was the multistate model a Markov Chain? What are the assumptions here? How were the state change rates estimated?

Reply: We included the information that a Markovian multistate analysis was used. A reference is given with regard to the applied statistcal methodolgy and the required assumptions (Lines 160-165 in track change modus version).

• It is also mentioned that a risk factor analysis (competing risk model) is used, where the different states of hospitalisation were not differentiated, but also in this case no much explanation is given. Are modelling assumptions met? (e.g. proportionality of hazards). If a multistate survival analysis was used already (where basically each transition rate is a survival model), why use a different survival model here, where states are not considered?

Reply: The simplified model was used to avoid biases from confounder treatment feedback. We wrote in the main manuscript: “For the risk factor analysis, we used a competing risks model to study effects on the time from hospitalisation to death in the hospital. To avoid collider bias, in this model, the different states of hospitalisation (regular ward, ICU, MV, ECMO) were not differentiated” (Lines 173-175 in track change modus version).

• Fine and Gray model was used in the results but not discussed in the methods. Please provide more information about this method, why it was employed and what assumptions it required.

Reply: We precisized in the main manuscript (Lines 177-180 in track change modus version): “Then, we estimated the subdistribution hazard ratio of death using a Fine and Gray model. The subdistribution hazard ratio quantifies the effect of risk factors on the absolute risks (rather than the rates) thereby combining the direct and indirect effects found in the cause-specific analysis.”

• In general, I would like to see more details of methods, such as what mathematical models were used, what assumptions, if the assumptions were met, citing appropriate literature, explaining why these method and not other competing methods were used.

Reply: As there is already a lot of methodological and technical information in the manuscript, we decided to give those interested in the methodology an up-to-date reference that includes a detailed summary of the chosen approach [Hazard D et al, Ref. 22], please see also lines 162-165: “Multistate model analysis has not only the major advantage that the time dyamics of a patients disease progression are taken into account but also that multiple events are studied simulanteuously. The model is shown in Fig S3. The statistical methodlogy and required assumptions are outlined in detail in (14).“

• Another major point missing is whether there was anything that could be learned from treatments? How did they affect the path to discharge or death of the patients? Were they taken into consideration as confounding variables of the models? It would be useful to add at least a remark that this was investigated, even if the results of the effect of treatment were unconclusive.

Reply: As mentionned above we report on patients from the first 2,5 months of the pandemic. No specific antiviral agent (such as remdesivir) was available at that time. Accordingly, results of the RECOVERY trial concerning potential benefits of patients treated with dexamthasone were not published yet, thus, no patient received corticosteroids with COVID-19/COVID-19-associated ARDS as indication. Moreover, as several randomized trials did not find any effects of lopinavir/ritonavir or hydroxychloroquin/chloroquine (and as the number of patients treated with these agents was too small), we refrained from performing in depth analyses with regard to pharmacological treatment strategies. This limitation is outlined in the discussion (lines 374-376).

• Code should be available for reproducibility for example by upload to a github account. At line 177, Rstudio version is not informative, it is just an advanced text editor for R code, authors should report which version of R was used and which R packages.

Reply: All analyses were performed with R Version 4.0.2. This information is provided in statistical methods. Moreover, the R-code for data analysis is now included in the supplementary material.

• I believe all data points should be available, not only summary data. The authors stated that all data were available but I was only able to find summary data. Could you provide a text file on github or an excel file as supplementary data?

Reply: Due to the German Federal Data Protection Act (Bundesdatenschutzgesetz) and the fact that the Institutional Review Board of the University Medical Center Freiburg granted publication of only anonymyzed data, inclusion of the complete dataset is not possible. We included a statement in the Ethical Consideration section.

• Is it possible to provide or estimate the false positive rate of the PCR-based test used to determine the SARS-CoV-2 infection? It could be interesting/relevant to know.

Reply: The specificity of the PCR-test used (RealStar SARS-CoV-2 RT-PCR kit 1.0 (Altona Diagnostics, Hamburg, Germany) is considered to be >99%.

Minor points:

• Line 49, please spell out ARDS and ECMO in the abstract and put the abbreviation in parenthesis. Please do this for all abbreviation when used the first time.

Reply: The abbreviations were introduced now throughout the manuscript.

• Line 86 I would remove “e.g.” as it seems unnecessary

Reply: Was removed.

• Line 305 “1 out of two ICU patients could was discharged alive”, remove “could” or change “could was” into “could be”.

• Line 307, please add a comma after “COVID-19”.

Reply: Both mistakes were corrected.

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Reviewer #1: No

Reviewer #2: No

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28 Oct 2020

COVID-19 in-hospital mortality and mode of death in a dynamic and non-restricted tertiary care model in Germany

PONE-D-20-25207R1

Dear Dr. Rieg,

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Congratulations your manuscript "COVID-19 in-hospital mortality and mode of death in a dynamic and non-restricted tertiary care model in Germany" is suitable for publication.

Reviewers' comments:

3 Nov 2020

PONE-D-20-25207R1

COVID-19 in-hospital mortality and mode of death in a dynamic and non-restricted tertiary care model in Germany

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