Analysis of predictors and outcomes of COVID-19 patients requiring ICU admission from COVID 19

doi:10.21203/rs.3.rs-1740554/v1

Background

Severe Corona virus disease (COVID-19) is associated with high mortality. Although single centre intensive care units (ICU) have reported clinical characteristics and outcomes, no large scale multicentric study from India has been published. The present retrospective, multi-centre study was aimed to describe the predictors and outcomes of COVID-19 patients requiring ICU admission from COVID-19 Registry of Indian council of Medical Research (ICMR), India.

Methods

Prospectively collected data from multiple participating institutions was entered in the electronic National Clinical Registry of COVID 19. We enrolled patients aged>18 years with COVID-19 pneumonia requiring ICU admission between March 2020 and August 2021. Exclusion criteria were negative RT PCR, death within 24 hours of ICU admission, or patients with incomplete data in the registry Their demographic characteristics, laboratory variables, ICU severity indices, treatment strategies and outcomes were analysed.

Results

A total of 5865 patients, with mean age 56±15 years, with 3840/5865 (65.4%) men, were enrolled in the ICMR registry.. Overall mortality was 2535/5865 (43.5%). Non-survivors were older than survivors (58.2±15.4 years vs 53.6 ±14.7 years; P=0.001). Non-survivors had multiple comorbidities (n=1951, 52.9%) with hypertension (47.2%) and diabetes (45.6%) being the most common, higher creatinine (1.6 ± P=0.001, high D-dimer (1.56 vs 1.37, P=0.001), higher CT severity index (16.8±5.2 vs 13.5 ±5.47 ) compared to survivors. Non survivors had longer hospital and ICU stay (P=0.001).

On multivariate regression analysis, high NLR (HR 1.017, 95% CI 1.005- 1.029, P=0.001), high CRP (HR 1.008, 95% CI 1.006- 1.010, P=0.001), high D dimer ((HR 1.089, 95% CI 1.065- 1.113, P=0.001) were associated with mechanical ventilation while younger age, (HR 0.974, CI 0.965-0.983, p=0.001), high D dimer (HR-1.014, CI 1.001-1.027, P=0.035) and use of prophylactic LMWH (HR 0.647, CI 0.527-0.794, p=0.001) were independently associated with mortality.

Conclusion

In this large retrospective study of 5865 critically ill COVID 19 patients admitted to ICU, overall mortality was 2535/5865 (43.5%). Age, high D dimer, CT Severity score and use of prophylactic LMWH were independently associated with mortality.

COVID-19

ICU patients

outcome

covid-19 registry

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic has affected approximately 225,024,781 persons, and claimed 4,636,153 lives till January 31, 2022.¹ The overall mortality in intensive care units (ICU) reported worldwide is approximately 40–50%.² The first case of COVID-19 was reported on January 27, 2020 in India.³ The Indian Council of Medical Research (ICMR) initiated a National Clinical Registry of COVID 19, to prospectively collect demographic, clinical, biochemical, radiological, therapeutic and outcome data of patients admitted to hospitals across India. As of February 2022, 4.5 crore patients have tested positive for COVID 19 and around 500,000 deaths have been reported.⁴ Till date, the United states has reported maximum number of cases and deaths followed by Brazil and India.

The mortality rate among hospitalised patients with COVID 19 has been estimated to be around 17% in a large metanalysis of 42 studies.⁵ However outcomes in critically ill patients remain poorly reported. Single centre studies from China and USA reported 53.8% and 39% mortality respectively, among critically ill patients.^6–8 In the COVID-ICU study from Europe, the investigators found an overall mortality of 31% among 4315 ICU patients and mortality of 37% in patients on mechanical ventilation.² There was a wide variation in the mortality rates reported from India. Non survivors accounted for 53% of the ICU admission in the study by Kerai et al⁹ and 26.1% by Zirpe et al.¹⁰Various studies across the world have reported both early and late predictors of mortality. The common predictors included higher age, BMI, SOFA scores, D dimer and lower PaO2 to FiO2 ratio.^2,11,12 Few Indian studies have also analysed predictors of mortality of which male gender, increasing CT score and need of mechanical ventilation were the prominent ones.¹⁰

Most data from India are single centre retrospective analysis with limited collection of therapeutic strategies and critical analyses of survival.¹⁰ The primary aim of this study was to analyse the characteristics and outcomes of patients admitted to various ICUs across India. We also investigated predictors of poor outcomes, utility of therapeutic strategies and factors predisposing to invasive mechanical ventilation in patients with COVID-19.

Study population and settings

We analysed the data of critically ill patients with COVID − 19 admitted to 53 ICUs across India between March 2020 to August 2021, as recorded in the National Clinical Registry of COVID-19 (NCRC). SARS-CoV-2 infection was confirmed by Real- Time- Polymerase- Chain Reaction (RT-PCR) assays, performed on nasopharyngeal swabs. Inclusion criteria were adults aged > 18 years admitted to ICU with confirmed infection based on RT-PCR testing. Exclusion criteria were negative RT-PCR result for COVID 19, patients dying within 24 hours of ICU admission, or patients with incomplete data in the registry.

Outcomes

Primary outcome was to analyse the demographic, clinical, biochemical, imaging, and severity characteristics of patients with severe COVID 19 pneumonia and compare among survivors and non survivors. Outcome measures such as predictors of invasive ventilation and mortality along with ICU and hospital length of stay were incorporated in the analysis. The study was conducted according to Declaration of Helsinki and ICMR-National Ethical Guidelines for Biomedical and Health Research involving Human Participants, 2017.

Data collection

Demographic details, medical history, laboratory results, radiological findings, vital parameters, treatment therapies (antivirals, steroids, immunomodulators, organ support devices) in-hospital complications and clinical outcomes of all patients admitted to ICU were entered in the ICMR COVID 19 Registry portal, available on http://icmrcovidregistry.nic.in and maintained by ICMR-National Institute of Medical Statistics All data was prospectively stored in electronic format and retrospectively analysed.

Case definition

Severe Covid-19 pneumonia was defined in patients presenting with fever, plus one of the following: respiratory rate > 30 breaths/min, breathlessness, and oxygen saturation by pulse oximetry (SpO2) < 90% on room air.¹³ Critically ill patients included those who had severe pneumonia, shock, and organ dysfunction syndrome at admission or during hospital stay.

Treatment protocols

The Government of India (GOI) /ICMR guidelines were followed for treating patients admitted in ICU. According to ICMR guidelines, oxygen therapy was titrated to target SpO2 > 92% with the use of oxygen delivery devices ranging from low flow devices (nasal prongs, simple face mask), high flow devices (venturi mask, high flow nasal cannula (HFNC), non-invasive ventilation (NIV)) and invasive mechanical ventilation. Standard medical care including steroids, Remdesivir and anticoagulation were administered as per ICMR protocol. Supportive care for critically ill patients in the form of advanced hemodynamic monitoring, haemodynamic support, enteral nutrition, glycaemic control, and stress ulcer prophylaxis were used in all eligible patients. Antibiotic and antifungal therapy was guided by cultures and sepsis markers. Renal replacement therapy and other supportive therapies/interventions were performed as per the clinical condition of the patients.

Statistical methods

Descriptive statistics are expressed as mean with standard deviation (SD) for parametric data and median with interquartile range (IQR) for non-parametric continuous data. Categorical data are expressed as numbers (n, %). We used Student’s t-test to compare continuous data between the two groups. Furthermore, we used Pearson’s Chi-square test or Fisher’s exact test (as appropriate) to compare categorical data among the two groups. Repeated measures of ANOVA were used to compare categorical data among three groups. The main outcomes are reported as estimated effect sizes along with precision (95% confidence intervals [CIs]). Cox proportional hazard analysis was performed for the predictors of invasive mechanical ventilation and mortality. Kaplan–Meier overall survival curves until Day 90 were computed and were compared using log-rank tests. Statistical significance was set at P < 0.05. All statistical tests were performed using SPSS ver. 25.0 (IBM Corp., Armonk, NY, USA).

A study flowchart is illustrated in Figure 1. Out of 29,509 patients entered in the National Clinical registry of COVID 19 till August 2021 , 5978 patients were assessed for eligibility, and 113 patients were excluded due to insufficient data. Finally, 5865 patients were included in the analysis, of whom 3330 survived (58.8%) (figure 1). The baseline demographic, clinical, laboratory and radiological characteristics in the two groups are shown in Table 1.

Table 1: Demographic, Clinical, Laboratory, and Radiographic findings on admission of critically ill COVID-19 patients treated in ICU.

Demographic and clinical characteristics	All patients	Non- survivors	Survivors	P value
Outcomes	5865	2535 (43.2%)	3330 (56.7%)
Age,*	55±15.18	58.2 ±15.4	53.63±14.7	0.001
12- 44 years	1371 (23.3)	494 (19.5)	877 (26.3)	0.001
45-59 years	1927 (32.8)	748 (29.5)	1179 (35.4)
>60 years	2513 (42.8)	1293 (51)	1274 (38.2)
Gender
Males	3840 (65.5)	1670 (65.8)	2170 (65)	0.404
Females	2023 (34.4)	865 (34)	1158 (34.7)
Transgender	2 (0.1)	0	2
BMI (kg/m2) N=1051 >30 (n %)	203(3.5)	76 (3)	127 (3)	0.112
>25 (n %)	983(16.8)	418 (16)	559 (17)	0.770
Comorbidity	3687 (62.8)	1951 (76.9)	1736 (4)	0.001
Hypertension	2303 (39.2)	1087 (43)	1216 (37)	0.818
Diabetes	1967 (33.5)	896 (35)	1071 (32)	0.434
Coronary artery disease	447 (7.6)	244 (10)	203 (6)	0.040
COPD	122 (2.0)	52 (2)	70 (2)	0.120
Chronic liver disease	63 (1.0)	42 (1.6)	21 (0.01)	0.02
Malignancy	55 (0.9)	28 (1)	27 (0.01)	0.571
Chronic kidney disease	312 (5.3)	171 (7)	141 (4)	0.394
Respiratory Rate*	23 ± 6.38	24.1±6.2 (23.86-24.4)	23.7±6.5 (23.52-24.06)	0.070
Heart rate* (N=5234)	94.8 ± 17.36	95.2±17.1	94.56±17.5	0.804
Blood pressure SBP*, mm Hg (n=5140)	127 ± 19	126.99±18.5	127.54±19.8	0.315
DBP* ,mm Hg (n=5111)	79 ± 11.46	79.4± 11.2 (78.9-79.8)	79.4±11.6 (79.0-79.9)	0.804
Symptoms
Cough with sputum	900	546 (22)	354 (11)	0.001
Shortness of breath	3985 (67.9)	2132 (84)	1853 (56)	0.023
Fever (Temp. ³ 37.3^oC)	3719 (63.4)	2166 (85)	1553 (47)	0.413
Diarrhoea	188 (3.2)	78 (3)	110 (3)	0.020
Anosmia	128 (2.1)	59 (2)	69 (2)	0.348
Altered sensorium	145 (2.4)	96 (4)	49 (1)	0.001
Haemoglobin, gm/dL*(N=3812)	12.07 ± 2.03	12.1±2.29	11.9 ±2.32	0.010
Total Leucocyte Count, × 109 /L* (N=3773)	7.5 (6.6-14.3)	10.5 (6.9-14.4)	9.1(6-14)	0.482
White blood cell count, × 109 / L (N=3840)
< 4	253 (6.5)	92 (36.4%)	161 (63.3%)	0.061
4-10	1622(42.2)	718 (44.3%)	904 (55.7%)
>10	1898(49.4)	823 (43.4%)	1075 (56.6% )
Neutrophil to lymphocyte Ratio (N=3317)	7.5 (4.2-13)	7.5 (4.8 – 17.3)	7.5 (3.9 – 15)	0.942
Platelet count, × 109 per L (N=3745)	208 (154-277)	200 (141- 265)	219 (150 – 280)	0.753
Bilirubin, mg/dl (N=3117)	0.6 (0.4-0.8)	0.68 (0.40 – 0.94)	0.70 (0.45 – 0.92)	0.711
Direct bilirubin	0.26 (0.19-0.46)	0.24 ( 0.16 - 0.40)	0.31 ( 0.20 – 0.41)	0.905
Albumin, g/dL (N=2520)	3.4 (3.0-3.7)	3.5 ( 3.1 – 3.9)	3.5 ( 3.2 – 3.9)	0.016
Creatinine, mg/dL (N=3330)	1 ( 0.9 – 1)	1.1 ( 0.8 – 1.62)	1.0 (0.8 – 1.6)	0.001
Lactate dehydrogenase, U/L (N=1989)	596 (408-843)	589 ( 377- 754)	519 ( 398 – 777)	0.515
D-dimer, μg/mL (N=1620)	1.26 (0.5-6.2)	1.56 ( 0.62 – 1.56)	1.37 ( 0.5 – 4.4)	0.015
Procalcitonin, (ng/mL) (N=682)	0.32 (0.15-0.99)	0.23 (0.13-1.35)	0.33 (0.16-0.81)	0.437
<0.1	71 (10.4)	35 (5.1)	36 (5.2)
≥ 0·1 to < 0·25	200 (29.3)	101(14.8)	99(14.5)
≥ 0·25 to < 0·5	144(21.1)	63(9.2)	71(10.4)
≥ 0 ·5	261(38.2)	124(18.18)	137(20.1)
Serum ferritin, ng/ml (N=1857)	641 (298-1062)	682 (399-1436)	609 (236-1289)	0.225
CRP, mg/l (N=2264)	77 (24-77)	69.05 (21.05-117.8)	36.7 (7.49-141.9)	0.248
<25	553 (24.4)	243 (10.7)	310 (13.6)	0.661
25-75	517 (22.8)	241(10.6)	276(12.1)
>75	1139 (50.3)	521(23.1)	618(27.2)
PT ,secs* (N=1518)	14.8 ± 4.79	14.86±5.23 (14.47-15.24)	14.83± 4.45 (14.5-15.1)	0.929
INR (N=1556)	1.1 (1.02-1.27)	1.13 (1.00-1.25)	1.02 (1.00-1.27)	0.430
APTT ,secs (N=1239)	29 (25-39)	26.8 (24.2-30)	30.8 (26.3-40.2)	0.597
CT severity score* (N=604)	14.6±5.5	16.8 ±5.2	13.5±5.47	0.001
< 8	75(12.4)	13(2.1)	62(10.2)
8-15	244(40.3)	52(8.6)	192(31.7)
>15	285(47.1)	139(23.1)	146(24.1)

Data Expressed as median (IQR), n (%),

*data expressed as mean±SD

P values were calculated by Mann-Whitney U test, χ2 test, or Fisher's exact test, as appropriate.

Glossary : BMI- body mass index, SBP- systolic blood pressure, DBP- diastolic blood pressure, HDU- high dependency unit, INR- international normalised ratio, APTT- activated partial thromboplastin time, PT-prothrombin time, CRP- C-reactive protein, ICU- Intensive care unit, CT, Computed Tomography

*Expressed as Mean ± SD

Comparison of clinical and laboratory parameters of survivor and non survivors.

Non survivors were significantly older in age as compared to survivors (58.2 vs 53.6 years, P=0.001). Co-morbidities like hypertension, diabetes, coronary artery disease, pulmonary disease were comparable in the two groups. Chronic liver disease was associated with higher risk of mortality (66.7% vs 33.3%, P=0.020, Table 1) Vital parameters- heart rate, respiratory rate, blood pressure at admission were comparable. Non survivors had significantly higher D-dimer (1.56 vs 1.36 ug/ml), higher CT severity score (16.8 vs 13.5 p=0.001) and lower albumin (3.38 vs 3.45 g/dL) at admission. (Table 1)

Treatments and Outcomes

Survivors had shorter median hospital stay compared to non survivors (8 [5-11] days vs 15 [9-21] days, P=0.001). Similarly, median ICU stay among survivors was significantly shorter in comparison to non survivors (4 [2-7] vs 5 [3-10] days, P=0.001). Use of dexamethasone, Remdesivir and low molecular weight heparin was associated with survival. Of the total patients analysed, a higher percentage of patients receiving high flow nasal cannula survived while outcomes with other oxygen therapies was comparable. Two patients received ECMO but none could survive. In hospital complications including septic shock, and hospital acquired pneumonia were comparable. (Table 2)

Table 2: Treatments and outcomes of critically ill COVID-19 patients treated in ICU

Outcomes	All patients	Non- survivors	Survivors	P-value
Duration of symptoms (in days)	5 (3-8)	5 (3- 8)	5 ( 3 -8)	0.120
Duration of hospital stay (in days) n=5865	9 (5-13)	10 (6-15)	8 (5-12)	0.001
Duration of ICU stay (in days)	7 (4-10)	5 (3-10)	4 (2-7)	0.001
Duration of HDU stay (in days)	1 (1-4)	3 (1-7.75)	1 (1-4)	0.001
Duration of ward stay (in days)	3 (1-6)	3 (2-6.5)	2 (1-3)	0.001
Time taken to resolution of major symptoms (in days)	7 (4-10)	8 ( 5-12)	7 ( 4 – 9)	0.001
Empiric Antibiotics*	1624 (73)	620 (24.5)	1004 (30)	0.790
Remdesivir*	3190 (89)	1275 (50)	1915 (58)	0.008
Dexamethasone*	3152 (85)	1445 (57)	1707 (51)	0.001
Tocilizumab*	228 (9.5)	90 (3.5)	138 (4)	0.500
Prophylactic LMWH*	2394 (41)	915 (36)	1479 (44)	0.001
Therapeutic LMWH*	2108 (36)	978 (39)	1130 (34)	0.001
High Flow nasal Cannula*	1118 (64)	491 (19.3)	627 (19)	0.028
Non-invasive mechanical ventilation*	1203 (71)	499 (20)	704 (21)	0.322
Invasive Mechanical ventilation*	1738 (81)	693 (27)	1045 (31)	0.710
ECMO*	2 (0.03)	2	0
Septic Shock*	633(10.7)	333 (13)	300 (9)	0.985
HAP*	389 (6.6)	170 (6.7)	219 (7)	0.165

Data expressed as median (IQR)

*Data expressed as n(%).P values were calculated by Mann-Whitney U test, χ2 test. or Fisher's exact test, as appropriate.

Glossary: ECMO=extracorporeal membrane oxygenation. ICU=intensive care unit, LMWH Low molecular weight Heparin. COVID-19: coronavirus disease 2019.

Predictors of Invasive Ventilation

On receiver operator characteristics (ROC) curves analysis (table 3) , the predictors of invasive mechanical ventilation were CRP > 75 mg/dL ( AUROC 0.663, sensitivity 64%, specificity 57%, P=0.001), D-dimer >1.16 ng/L AUROC 0.628, sensitivity 64.2%, specificity 54%, P=0.001), ferritin >500 ng/ml (AUROC 0.641, sensitivity 69.6%, specificity 57.7%, P=0.001).

Table 3: Predictors of invasive ventilation using ROC (Receiver operating characteristics), (N=1738)

Variable cut-off	AUC	Sensitivity ( %)	Specificity ( %)	P value
CRP (>75 mg/l)	0.663 (0.617-0.7090	64.0	57.1	0.001
D-dimer (>1.6 ng/l)	0.628 (0.577-0.680)	64.2	54.0	0.001
Ferritin (>500 ng/ml)	0.641 (0.588-0.693)	69.6	52.7	0.001

CRP = C Reactive protein

COX univariate analysis demonstrated haemoglobin, NL ratio, ESR, CRP, aPTT, creatinine and ferritin were associated with invasive mechanical ventilation. On multivariate regression analysis, high NLR (HR 1.017, 95% CI 1.005- 1.029, P=0.001), high CRP (HR 1.008, 95% CI 1.006- 1.010, P=0.001), high D dimer ((HR 1.089, 95% CI 1.065- 1.113, P=0.001) were associated with mechanical ventilation. (Table 4)

Table 4: Cox Proportional Hazards Analysis for invasive ventilation using relevant parameters, and two predictive models.

Univariable Analysis				Multivariate Analysis
Parameter	Hazard Ratio	95% CI	P value	Parameter	Hazard Ratio	95% CI	P value
				Model 1
Haemoglobin	0.96	0.93-099	0.027	NLR	1.017	1.005-1.029	0.004
NLR	1.017	1.017-1.012	0.000	CRP	1.008	1.006-1.010	0.000
ESR	1.011	1.008-1.014	0.000	D Dimer	1.089	1.065-1.113	0.000
CRP	1.006	1.004-1.007	0.000	Model 2
aPTT	1.20	1.07-1.35	0.000	Heart Rate	1.014	1.007-1.022	0.000
Creatinine	1.11	1.06-1.16	0.000	NLR	1.014	1.000-1.024	0.004
Ferritin	1.00	1.00-1.00	0.001	CRP	1.007	1.005-1.009	0.000

NLR= Neutrophil Lymphocyte Ratio, ESR=Erythrocyte sedimentation rate. AST= Aspartate aminotransferase, CRP = C Reactive protein, aPTT= activated partial thromboplastin time

Predictors of mortality

On receiver operator characteristics (ROC) curves analysis predictor of mortality were CTSI (CT severity index) >13.5 (AUROC 0.681, sensitivity 77%, specificity 49%, P=0.001) , age > 55 years (AUROC 0.589, sensitivity 60.6% specificity 54%, P=0.001) (table 5)

Table 5: Predictors of Mortality among ICU patients, (N=2535)

Variable cut-off	AUC	Sensitivity ( %)	Specificity ( %)	P value
Age (>55 years)	0.589 (0.574-0.604)	60.6	54	0.001
Hb (<11.7 mg/l)	0.527 (0.509-0.546)	62.2	42	0.004
RR (>22 breaths/min)	0.527 (0.509-0.546)	61.3	41	0.003
CTSI (>13.5)	0.681 (0.636-0.727)	77	49	0.001

RR = Respiratory Rate (breaths/min), Hb = Haemoglobin (mg/l). CTSI=Computed Tomographic severity Index Score.

COX univariate analysis depicted age, use of dexamethasone, remdesivir, LMWH, creatinine, D dimer, use of High flow nasal cannula and continuous positive airway pressure (CPAP) were associated with mortality. On multivariate regression analysis, younger age, (HR 0.974, CI 0.965-0.983, p=0.001), high D dimer (HR-1.014, CI 1.001-1.027, P=0.035) and use of prophylactic LMWH (HR 0.647, CI 0.527-0.794, p=0.001) were independently associated with mortality (table 6).

Table 6: Results of Cox Proportional Hazards Analysis for mortality of critically ill COVID-19 patients treated in ICU using relevant parameters, and predictive model.

Parameter	Univariate Analysis		Parameter	Multivariate Analysis (Model 1)
Parameter	OR (95% CI)	p-value	Parameter	OR (95% CI)	p-value
Age	0.98 (0.97-0.98))	<0.001	Age	0.974(0.965-0.983)	<0.001
Use of Dexamethasone	0. 63(0.52-0.77)	<0.001	D dimer	1.014 (1.001-1.027)	0.035
Use of Remdesivir	0.71 (0.55-0.91)	0.008	Prophylactic LMWH	0.647 (0.527-0.794)	0.001
Prophylactic LMWH	0.71 (0.55-0.91)	<0.001
Therapeutic LMWH	1.22 (1.09 -1.36)	<0.001
HFNO	0.79 (0.65-0.97)	0.026
CPAP	0.73 (0.57-0.92)	0.01
Creatinine	1.08(1.04 - 1.13)	0.001
D-Dimer	1.01(1.0 - 1.03)	0.016

LMWH- low molecular weight heparin, HFNO- high flow nasal oxygen, CPAP- continuous positive airway pressure

Overall survival at day 90 was 58.8%. Patients with age > 60 years, (figure 2) and CT severity index > 15 had lower survival at day 90 on Kaplan Meir survival analysis. (Figure 3)

This is one of the largest retrospective studies from India among COVID-19 patients who require ICU admission. The comprehensive information on their baseline characteristics and short term mortality were analysed from data obtained from National clinical registry of Covid -19.

Overall mortality in our cohort was 43.2% that is higher than mortality rates reported from across the world. In the study by COVID ICU group from Europe across 138 hospitals enrolling more than 4000 critically ill patients², the 90-day mortality reported was 31%, where as a multi-centric study from china, the 60 day mortality was 61.5 %.¹³ Gupta et al ¹⁴ reported mortality of 35.4% at 28 day among critically ill patients from United States of America. The wide variations among different countries may be explained by the variability in the level of health care infrastructure, burden of co-morbid illness and restricted availability of effective therapeutic strategies at the early part of the pandemic.

In our study, patients with multiple co-morbidities have higher mortality rates although when analysed individually, only Chronic Liver disease was associated with poor outcome although data was available for a very small number of patients. Literature on effect of comorbidities especially on ICU patients is scarce and conflicting. Petrilli et al ¹⁵ found that age and co-morbidities strongly predicted hospital admission and only a weak association was found with critical illness or death. However, the COVID ICU group ² found significant correlation between diabetes, hypertension and older age with mortality, not unlike our study.

Among laboratory parameters, lower haemoglobin, low albumin, high D dimers and high creatinine were significantly more amongst non survivors, however, only elevated D-Dimer was found to be independently associated with mortality. COVID-ICU group ² and many case series ^{7, 16-19} also found D dimers as a significant contributor to mortality. This may reflect the underlying COVID 19 induced hyper-coagulable state pre-disposing the patients to macro and micro-thrombosis of major organs, leading to multiorgan dysfunction and resultant mortality.

Elevated D-dimer warrants early administration of prophylactic anticoagulation. Our study demonstrated better outcomes with prophylactic anticoagulation while therapeutic anticoagulation was associated with increased mortality on univariate analysis. Although we did not have data regarding the bleeding/thromboembolic events, our results corroborates finding of the ACTION trial that use of therapeutic anticoagulation had no clinical benefit.²⁰

It is pertinent to identify the determinants of outcomes of critically ill patients as this will help in optimizing the use of ICU care and other resources especially in resource constraint countries like India. In our study, the predictors of mortality with acceptable sensitivity and specificity were age > 55 years, haemoglobin less than 11.7 mg/dl and CTSI > 13.5. Among these parameters CTSI had the highest predictive accuracy. Tabatabaei et al ²¹have also reported CTSI as a predictor of mortality however, their reported cut off of 7.5 (sensitivity of 0.83, specificity of 0.87) is much lower. Non availability of CTSI in all of ICU patients at admission due to operational issues could be one of the factors associated with this discrepancy.

The studies from Europe reported significantly higher rates of intubations in ICU patients ². The COVID-ICU² study group reported that 63% of ICU patients were intubated within the first 24 hours while overall 80% patients received mechanical ventilation. In contrast, studies from China²³reported a 47% rate of intubation among critically ill patients. In a large multicentric study from America¹⁴, the overall rate of mechanical ventilation was 67%. Our study reports relatively lower percentage (31%) of patients requiring invasive mechanical ventilation compared to other countries. The discrepancy may be explained by the intensivist’s reluctance to initiate mechanical ventilation owing to limited resources and manpower at the peak of the pandemic. This practice of delaying or avoiding early intubation may have been influenced by lower rates of intubation in American or Chinese studies and could have been responsible for altering the mortality.

Apart from mortality, we also analyzed the predictors for invasive ventilation. High NLR, CRP and D-Dimer at admission were independently associated with need for invasive mechanical ventilation. Similar findings have been reported in a large number of cohort studies ^23,24. These findings support the importance of elevated inflammatory parameters in the progression of disease. CRP more than 75 mg/dL has been proposed as a parameter that predicts progression of disease thus requiring not only escalation of oxygen but also the need of more agressive immunosuppression.²⁵

In the initial phase of the COVID 19 pandemic, there was a paucity of reliable treatement options due to the novelty of the disease and the evolving treatment paradigms. As more data emerged, efficacy of low dose dexamethasone ²⁶and interleukin 6 inhibitors ²⁷ was established. These agents were not part of therapeutic strategies intitially but subsequently these drugs were included in the treatment guidelines by the ICMR.¹¹ In our analysis, we did find reduced mortality with use of dexamethasone and antiviral drug (remdesivir). In our study, IL6 inhibitor (tocilizumab) did not affect the outcomes, but this discripancy could be due to lack of data regarding timing, dose or mode of administration of the drug.

The strength of the study is the exhaustive data of the physiological, clinical, laboratory, radiological and outcome data of more than 5870 critically ill patients admitted in multiple centres/ICUs across India. We recognise several limitations to our study. Although the data in ICMR COVID registery was to be collected prospectively but due to operational difficulties data was collected retrospectively in many centres. At the height of the pandemic, the medical facilities across India were severely overburdened. So uniformity in admission criteria and treatment modalities across different ICUs cannot be ascertained. Third, missing data due to large number of patients getting admitted and difficulty in capturing all the details especially during peak of crisis may have been a confounder. Fourthly, the data capture forms had been designed so as to reflect the commonly recorded parameters in all hospital case record forms. Absence of status of oxygenation at admission indices like ratio of partial pressure of arterial oxygen to fraction of inspired oxygen (PFR), ratio of percentage oxygen saturation to fraction of inspired oxygen (SFR), ratio of oxygen saturation /fraction of inspired oxygen to respiratory rate (ROX index) could not be calculated due to absence of data. Moreover, various scores for major organ dysfunction at admission like SOFA (sequencial organ function assssment), APACHE (acute physiology and chronic health evaluation) , SAPS II (simplified acute physiology score) score could not be computed due to lack of data on some important variables. Data on ICU complications like VAP (ventilator assosciated pneumonia), CLABSI (central line assosciated blood stream index) , CAUTI (catheter assosciated urinary tract infection), RRT (renal replacment therapy) could not be analysed.

In this retrospective study of laboratory confirmed 5865 COVID 19 patients admitted to ICU in India, overall mortality was 43.2%. Mortality was higher in older age and patients with multiple comorbidities. High D Dimer and high CT severity index at admission were important predictors of mortality in critically ill patients treated in ICU. Inflammatory markers like CRP, high NLR and D dimer independently predicted the need for invasive ventilation. This information maybe useful in early triaging and resource management in future outbreaks of similar kinds.

Author Contributions:

AB is the guarantor.

Study design, data analysis, data interpretation and manuscript writing team: Postgraduate Institute of Medical Education & Research: KK, KS, GDP, AB, MP, VM. Indian Council of Medical Research: AM, GK, AT. Gandhi Medical College: TCB. AIIMS Jodhpur: PB. CMC Ludhiana: MJ. National Institute of Medical Statistics: GRM, DS, SP, VVR.

Monitoring of the study: Indian Council of Medical Research: AM, GK, AT, National Institute of Medical Statistics: GRM, DS, VVR.

Patient enrolment, conduct of study, clinical care and data collection: Postgraduate Institute of Medical Education & Research: KK, KS, GDP, AB, GDP, MP, VM. Gandhi Medical College: TCB, RM. MAM, VSA. AIIMS Jodhpur: PB, MKG, ADG. CMC Ludhiana: MJ, VL, MT. Shaheed Nirmal Mahato Medical College & hospital: UKO, RRJ. Gulbarga Institute of Medical Sciences: VS, SA. RDG Medical College: AP, AS, MP. King George Medical University: HD, AG, VK. Hi tech Medical College: LS, MR. AIIMS Raipur: TDB, PKK, AK. AIIMS Bhopal: RJ, SS, AG. NHL, Municipal Medical College: JKK, NMS. CIMS hospital: AP, SM, NS. PGIMS Rohtak: VKK, DS, SG. Dhiraj Hospital & Sumandeep Vidyapeeth, Vadodara: AS, AC, BP. Bowring & Lady Curzon Hospital: KYML, DVP, CGS. National Institute Of Mental Health And Neurosciences: AD GMERS Medical College: MP, MA, PT. AIIMS Bhubaneswar: SB, SKB. DY Patil Medical College, Hospital & Research centre: SKS, PL, ST. North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences: VN, SP, MJ. SP Medical College, Bikaner: BKG, JG. GIMS, Noida: RU, SS. GMC Bhopal: SD, PD, RS. Jawaharlal Nehru Medical College: MS, NF, SA. Institute of Postgraduate Medical education & Research: SG, AH. Infectious Disease And Beliaghata Hospital: YR. Medical College Kilkata: AT; Late BRK Memorial Medical College: ND. Dept. of health & family Welfare, Nagaland: NK. College Of Medicine and Sagore Dutta Hospital: SM, SM. IMS & SUM Hospital: LP. SMS Medical College: SB, AA; MGM Medical College: RV, NS. ESI Santhnagar: RKB, MAM. Banaras Hindu University: JC. The Medicity Hospital: SK. St. Johns Medical College: RC. Tata Medical Centre: SC. Kakatiya Medical College: MPK.

Acknowledgements

Kamal Kajal & Karan Singla, Goverdhan Dutt Puri, Ashish Bhalla, Aparna Mukherjee, Gunjan Kumar, Alka Turuk,, Madhumita Premkumar, Varun Mahajan, Thrilok Chander Bingi, Pankaj Bhardwaj, Mary John, Geetha R Menon, Damodar Sahu, Samiran Panda & Vishnu Vardhan Rao

And

NCRC study group

Rajarao Mesipogu, Mohammed Ayaz Mohiuddin , Vinaya Sekhar Aedula, Manoj K Gupta, Akhil D Goel, Vikas Loomba, Maria Thomas, U K Ojha, R R Jha, Veeresh Salgar , Santosh Algur, Ashish Pathak, Ashish Sharma , Manju Purohit, Himanshu Dandu, Amit Gupta, Vivek Kumar, Lisa Sarangi, Mahesh Rath, Tridip Dutta Baruah, Pankaj Kumar Kannauje, Ajit Kumar, Rajnish Joshi, Saurabh Saigal, Abhishek Goel, Janakkumar R Khambholja , Amit Patel, Surabhi Madan, Nitesh shah , V K Katyal, Deepinder Singh, Sandeep Goyal, Arti Shah, Amit Chauhan , Bhavesh Patel, Kala Yadhav M L , Dayananda V P, Chetana GS, Anita Desai, Manisha Panchal, Mayank Anderpa, Payal Tadavi, Sourin Bhuniya, Manoj Kumar Panigrahi, Shakti Kumar Bal, Sachin K Shivnitwar, Prajakta Lokhande, Srikanth Tripathy, Vijay Nongpiur, Star Pala, Md Jamil, Bal Kishan Gupta, Jigyasa Gupta, Rashmi Upadhyay, Saurabh Srivastava, Simmi Dube, Preksha Dwivedi, Rita Saxena, Mohammed Shameem, Nazish Fatima, Shariq Ahmed, Nehal M. Shah, Soumitra Ghosh, Yogiraj Ray, Avijit Hazra, Arunansu Talukdar, Naveen Dulhani, Nyanthung Kikon, Subhasis Mukherjee, Susenjit Mallick, Lipilekha Patnaik, Sudhir Bhandari, Abhishek Agrawal, Rajaat Vohra, Nikita Sharma, Rajiv Kumar Bandaru, Mehdi Ali Mirza, Jaya Chakravarty, Sushila Kataria, Ratnamala Choudhury, Soumyadip Chatterji, M.Pavan Kumar

All Authors to be individually searchable through their pubmed records.

Ethical Approval statement:

Approval was obtained from the Central Ethics Committee for Human Research at ICMR as well as from the respective Institutional Ethics Committee of each of the participating centres. Considering the observational nature of the registry, and collection of anonymised data being done primarily from the routine case records of the patients, a waiver of consent was granted by the Ethics Committees.

Availability of data and materials:

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests:

AM, GK, AT and SP are employed by the Indian Council of Medical Research (ICMR), the funding source for the study.

Funding Source

Indian Council of Medical Research, Delhi

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No competing interests reported.

Analysis of predictors and outcomes of COVID-19 patients requiring ICU admission from COVID 19

Status:

Version 1

Abstract

Figures

Introduction

Methods

Study population and settings

Outcomes

Data collection

Treatment protocols

Statistical methods

Results

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1