Introduction

This is an ongoing report of case series describing COVID+ patients at a single center.

Select the code button in this section to see the R code for dataset construction.

Cohort Selection

# bring in covid
load("./Data/covid_master.RData")
# bring in covid1, cleaned set
load("./Data/covid_cleaned.RData")

mytables <- tableby.control(test = T, total = T, digits = 0, digits.pct = 0)

# Daily and weekly data summary for graphing progress
covid.time.series <- covid %>% filter(is.na(sarscov2_status) == F) %>% filter(is.na(sarscov2_collected_date) == 
    F) %>% filter(sarscov2_collected_date > "2020-03-08" & sarscov2_collected_date < 
    "2020-05-18") %>% filter(is.na(delete) == T)

covid.daily <- covid.time.series %>% group_by(sarscov2_collected_date) %>% summarise(N = n(), 
    Positive = sum(sarscov2_status == "Positive"), Negative = sum(sarscov2_status == 
        "Negative"), Percent.positive = round(Positive/N * 100, digits = 1)) %>% 
    mutate(Cum.N = cumsum(N), Cum.P = cumsum(Positive))

# Weekly data summary for graphing progress and for creating strata for sampling
covid.weekly <- covid.time.series %>% group_by(week) %>% summarise(N = n(), Positive = sum(sarscov2_status == 
    "Positive"), Negative = sum(sarscov2_status == "Negative"), Percent.positive = round(Positive/N * 
    100, digits = 1)) %>% mutate(Cum.N = cumsum(N), Cum.P = cumsum(Positive))

As of 2020-10-29 the total number of observations in dataset = 4402

Exclusions:
- No valid test result (N = 2)
- No valid test date (N = 145)
- Test date < March 9 or after May 17 (N = 52)
- Test date > 5 days after admission (N = 15)
- Test result not positive for SARS-CoV2 (N = 3950)

Final cohort for all tests analysis N = 4138
Final cohort for COVID+ analysis N = 359

Figures for all COVID Tests

Daily Total

ggplot(covid.daily, aes(x = sarscov2_collected_date)) + geom_bar(aes(y = N), stat = "identity", 
    fill = "dark grey", color = "black") + geom_bar(aes(y = Positive), stat = "identity", 
    fill = "dark red", color = "black") + geom_text(aes(y = N, label = N), nudge_y = 20, 
    angle = 90) + geom_text(aes(y = 0, label = Positive), nudge_y = -20, color = "dark red", 
    angle = 90) + theme_bw() + scale_x_date(breaks = "1 week", date_labels = "%b %d") + 
    labs(title = "Total Daily Tests Performed", x = "Date of SARS-CoV- 2 Test", y = "Number of tests") + 
    theme(axis.title = element_text(size = 14), axis.text.x = element_text(angle = 60, 
        hjust = 1, size = 12), axis.text.y = element_text(size = 12))

Daily Cumulative

ggplot(covid.daily, aes(x = sarscov2_collected_date)) + geom_bar(aes(y = Cum.N), 
    stat = "identity", fill = "dark grey", color = "black") + geom_bar(aes(y = Cum.P), 
    stat = "identity", fill = "dark red", color = "black") + geom_text(aes(y = Cum.N, 
    label = Cum.N), nudge_y = 500, angle = 90, color = "black") + geom_text(aes(y = 0, 
    label = Cum.P), nudge_y = -500, angle = 90, color = "dark red") + theme_bw() + 
    scale_x_date(breaks = "1 week", date_labels = "%b %d") + labs(title = "Cumulative Number of Tests Performed", 
    x = "Date of SARS-CoV- 2 Test", y = "Number of tests") + theme(axis.title = element_text(size = 14), 
    axis.text.x = element_text(angle = 60, hjust = 1, size = 12), axis.text.y = element_text(size = 12))

Daily Percent

ggplot(covid.daily, aes(x = sarscov2_collected_date)) + geom_bar(aes(y = Percent.positive), 
    stat = "identity", fill = "dark red", color = "black") + geom_text(aes(y = Percent.positive, 
    label = Percent.positive), nudge_y = 10, angle = 90) + theme_bw() + scale_x_date(breaks = "1 week", 
    date_labels = "%b %d") + labs(title = "Percent of Daily Tests Performed Resulted Positive", 
    x = "Date of SARS-CoV- 2 Test", y = "%") + theme(axis.title = element_text(size = 14), 
    axis.text.x = element_text(angle = 60, hjust = 1, size = 12), axis.text.y = element_text(size = 12))

Weekly Total

ggplot(covid.weekly, aes(x = week)) + geom_bar(aes(y = N), stat = "identity", fill = "dark grey", 
    color = "black") + geom_bar(aes(y = Positive), stat = "identity", fill = "dark red", 
    color = "black") + geom_text(aes(y = N, label = N), nudge_y = 30, angle = 90) + 
    geom_text(aes(y = 0, label = Positive), nudge_y = -30, color = "dark red", angle = 90) + 
    theme_bw() + # scale_x_date(breaks = '1 week', date_labels = '%b %d')+
labs(title = "Total Weekly Tests Performed", x = "Monday of Week of SARS-CoV- 2 Test", 
    y = "Number of tests") + theme(axis.title = element_text(size = 14), axis.text.x = element_text(angle = 60, 
    hjust = 1, size = 12), axis.text.y = element_text(size = 12))

Weekly Cumulative

ggplot(covid.weekly, aes(x = week)) + geom_bar(aes(y = Cum.N), stat = "identity", 
    fill = "dark grey", color = "black") + geom_bar(aes(y = Cum.P), stat = "identity", 
    fill = "dark red", color = "black") + geom_text(aes(y = Cum.N, label = Cum.N), 
    nudge_y = 1000, angle = 90, color = "black") + geom_text(aes(y = 0, label = Cum.P), 
    nudge_y = -1000, angle = 90, color = "dark red") + theme_bw() + labs(title = "Cumulative Number of Tests Performed", 
    x = "Monday Date of Week of SARS-CoV- 2 Test", y = "Number of tests") + theme(axis.title = element_text(size = 14), 
    axis.text.x = element_text(angle = 60, hjust = 1, size = 12), axis.text.y = element_text(size = 12))

Weekly Percent

ggplot(covid.weekly, aes(x = week)) + geom_bar(aes(y = Percent.positive), stat = "identity", 
    fill = "dark red", color = "black") + geom_text(aes(y = Percent.positive, label = Percent.positive), 
    nudge_y = 10, angle = 90) + theme_bw() + # scale_x_date(breaks = '1 week', date_labels = '%b %d')+
labs(title = "Percent of Weekly Tests Performed Resulted Positive", x = "Monday Date of Week of SARS-CoV- 2 Test", 
    y = "%") + theme(axis.title = element_text(size = 14), axis.text.x = element_text(angle = 60, 
    hjust = 1, size = 12), axis.text.y = element_text(size = 12))

Tables for Mortality

Demographics

summary(tableby(died ~ age + age_cat + sex + race_adj + ethnic + bmi + bmi_cat + 
    covid_contact + home, data = covid1, control = mytables))

	Alive (N=306)	Died (N=53)	Total (N=359)	p value
Age, years				< 0.001
Mean (SD)	55 (17)	73 (12)	58 (18)
Range	17 - 101	46 - 96	17 - 101
age_cat				< 0.001
<20	7 (2%)	0 (0%)	7 (2%)
>/=80	19 (6%)	15 (28%)	34 (9%)
20-39	55 (18%)	0 (0%)	55 (15%)
40-59	110 (36%)	6 (11%)	116 (32%)
60-79	115 (38%)	32 (60%)	147 (41%)
Sex				0.028
Female	142 (46%)	16 (30%)	158 (44%)
Male	164 (54%)	37 (70%)	201 (56%)
Race				0.514
N-Miss	1	0	1
Black	239 (78%)	46 (87%)	285 (80%)
Other	4 (1%)	0 (0%)	4 (1%)
Unknown	8 (3%)	1 (2%)	9 (3%)
White	54 (18%)	6 (11%)	60 (17%)
Ethnicity				0.310
N-Miss	5	1	6
Hispanic	38 (13%)	4 (8%)	42 (12%)
Non-Hispanic	263 (87%)	48 (92%)	311 (88%)
BMI,kg/m2				0.440
N-Miss	11	1	12
Mean (SD)	30 (16)	28 (7)	29 (15)
Range	0 - 239	16 - 44	0 - 239
BMI,kg/m2 categories				0.297
N-Miss	11	1	12
<18.5	22 (7%)	4 (8%)	26 (7%)
18.5-24.9	77 (26%)	20 (38%)	97 (28%)
25-29.9	82 (28%)	13 (25%)	95 (27%)
30+	114 (39%)	15 (29%)	129 (37%)
Contact with suspected/confirmed COVID-19 case				0.045
N-Miss	1	0	1
No	218 (71%)	30 (57%)	248 (69%)
Yes	63 (21%)	14 (26%)	77 (22%)
Unknown	24 (8%)	9 (17%)	33 (9%)
Housing type				< 0.001
N-Miss	1	0	1
Correctional facility	14 (5%)	0 (0%)	14 (4%)
Homeless/shelter	17 (6%)	0 (0%)	17 (5%)
Long term health facility	7 (2%)	1 (2%)	8 (2%)
Nursing home	59 (19%)	39 (74%)	98 (27%)
Stable Home	206 (68%)	13 (25%)	219 (61%)
Unknown	2 (1%)	0 (0%)	2 (1%)

Comorbidity

summary(tableby(died ~ htn + diabetes + chf + cad + stroke + dementia + ckd + cirrhosis + 
    copd + asthma + other_lung + osa + malignancy + transplant + hiv + art + pregnant, 
    data = covid1, control = mytables))

	Alive (N=306)	Died (N=53)	Total (N=359)	p value
Hypertension				0.001
No	116 (38%)	8 (15%)	124 (35%)
Yes	190 (62%)	45 (85%)	235 (65%)
Diabetes mellitus				0.004
N-Miss	0	1	1
IDDM	43 (14%)	4 (8%)	47 (13%)
NIDDM	53 (17%)	11 (21%)	64 (18%)
No	210 (69%)	35 (67%)	245 (68%)
Unknown	0 (0%)	2 (4%)	2 (1%)
Congestive heart failure				0.038
N-Miss	1	1	2
No	263 (86%)	39 (75%)	302 (85%)
Yes	42 (14%)	13 (25%)	55 (15%)
Coronary artery disease				0.859
N-Miss	1	1	2
No	278 (91%)	47 (90%)	325 (91%)
Yes	27 (9%)	5 (10%)	32 (9%)
Stroke				< 0.001
No	245 (80%)	27 (51%)	272 (76%)
Yes	61 (20%)	26 (49%)	87 (24%)
Dementia				< 0.001
No	258 (84%)	24 (45%)	282 (79%)
Yes	47 (15%)	29 (55%)	76 (21%)
Unknown	1 (0%)	0 (0%)	1 (0%)
Chronic kidney disease				0.004
No	253 (83%)	40 (75%)	293 (82%)
Unknown	0 (0%)	1 (2%)	1 (0%)
Yes	29 (9%)	11 (21%)	40 (11%)
Yes, on dialysis	24 (8%)	1 (2%)	25 (7%)
Cirrhosis				0.564
N-Miss	1	0	1
No	302 (99%)	52 (98%)	354 (99%)
Yes	3 (1%)	1 (2%)	4 (1%)
COPD				0.277
No	289 (94%)	48 (91%)	337 (94%)
Yes	17 (6%)	5 (9%)	22 (6%)
Asthma				0.236
No	280 (92%)	51 (96%)	331 (92%)
Yes	26 (8%)	2 (4%)	28 (8%)
Structural lung disease				0.740
No	302 (99%)	52 (98%)	354 (99%)
Yes	4 (1%)	1 (2%)	5 (1%)
OSA/OHS				0.678
N-Miss	2	0	2
No	288 (95%)	49 (92%)	337 (94%)
Yes	15 (5%)	4 (8%)	19 (5%)
Unknown	1 (0%)	0 (0%)	1 (0%)
Malignancy				0.780
N-Miss	1	0	1
Current	10 (3%)	1 (2%)	11 (3%)
No	286 (94%)	51 (96%)	337 (94%)
Prior	9 (3%)	1 (2%)	10 (3%)
Solid organ or stem cell transplant				0.677
No	305 (100%)	53 (100%)	358 (100%)
Yes	1 (0%)	0 (0%)	1 (0%)
HIV/AIDs				0.283
No	292 (95%)	53 (100%)	345 (96%)
Yes	13 (4%)	0 (0%)	13 (4%)
Unknown	1 (0%)	0 (0%)	1 (0%)
Prescribed antiretroviral therapy
N-Miss	293	53	346
No	1 (8%)	0	1 (8%)
Yes	12 (92%)	0	12 (92%)
Pregnant				0.300
N-Miss	164	37	201
No	133 (94%)	16 (100%)	149 (94%)
Yes	9 (6%)	0 (0%)	9 (6%)

Symptoms

summary(tableby(died ~ time2test + time2hospital + fever + cough + sob + myalgias + 
    chills + fatigue + sore_throat + diarrhea + nausea + vomit + headache + anosmia + 
    ageusia + abdominal_pain + ams, data = covid1, control = mytables))

	Alive (N=306)	Died (N=53)	Total (N=359)	p value
ED to test date, days				0.912
Mean (SD)	0 (1)	0 (1)	0 (1)
Range	-4 - 4	0 - 4	-4 - 4
Symptoms to ED, days				< 0.001
N-Miss	5	2	7
Mean (SD)	6 (7)	2 (4)	5 (7)
Range	-3 - 61	-3 - 21	-3 - 61
Fever				< 0.001
N-Miss	2	0	2
No	128 (42%)	13 (25%)	141 (39%)
Yes	153 (50%)	22 (42%)	175 (49%)
Unknown	23 (8%)	18 (34%)	41 (11%)
Cough				< 0.001
N-Miss	1	0	1
No	93 (30%)	13 (25%)	106 (30%)
Yes	189 (62%)	17 (32%)	206 (58%)
Unknown	23 (8%)	23 (43%)	46 (13%)
Dyspnea				< 0.001
N-Miss	2	0	2
No	136 (45%)	12 (23%)	148 (41%)
Yes	145 (48%)	28 (53%)	173 (48%)
Unknown	23 (8%)	13 (25%)	36 (10%)
Myalgias				< 0.001
N-Miss	2	0	2
No	175 (58%)	19 (36%)	194 (54%)
Yes	99 (33%)	7 (13%)	106 (30%)
Unknown	30 (10%)	27 (51%)	57 (16%)
Chills				< 0.001
N-Miss	1	0	1
No	187 (61%)	21 (40%)	208 (58%)
Yes	88 (29%)	8 (15%)	96 (27%)
Unknown	30 (10%)	24 (45%)	54 (15%)
Fatigue				< 0.001
N-Miss	1	0	1
No	168 (55%)	16 (30%)	184 (51%)
Yes	107 (35%)	10 (19%)	117 (33%)
Unknown	30 (10%)	27 (51%)	57 (16%)
Sore throat				< 0.001
N-Miss	1	0	1
No	246 (81%)	24 (45%)	270 (75%)
Yes	29 (10%)	2 (4%)	31 (9%)
Unknown	30 (10%)	27 (51%)	57 (16%)
Diarrhea				< 0.001
N-Miss	1	0	1
No	200 (66%)	23 (43%)	223 (62%)
Yes	78 (26%)	5 (9%)	83 (23%)
Unknown	27 (9%)	25 (47%)	52 (15%)
Nausea				< 0.001
N-Miss	1	0	1
No	215 (70%)	25 (47%)	240 (67%)
Yes	62 (20%)	3 (6%)	65 (18%)
Unknown	28 (9%)	25 (47%)	53 (15%)
Vomiting				< 0.001
N-Miss	1	0	1
No	237 (78%)	25 (47%)	262 (73%)
Yes	41 (13%)	4 (8%)	45 (13%)
Unknown	27 (9%)	24 (45%)	51 (14%)
Headache				< 0.001
N-Miss	2	0	2
No	224 (74%)	26 (49%)	250 (70%)
Yes	50 (16%)	0 (0%)	50 (14%)
Unknown	30 (10%)	27 (51%)	57 (16%)
Loss of smell				< 0.001
N-Miss	1	0	1
No	257 (84%)	25 (47%)	282 (79%)
Yes	17 (6%)	0 (0%)	17 (5%)
Unknown	31 (10%)	28 (53%)	59 (16%)
Loss of taste				< 0.001
N-Miss	1	0	1
No	249 (82%)	26 (49%)	275 (77%)
Yes	25 (8%)	0 (0%)	25 (7%)
Unknown	31 (10%)	27 (51%)	58 (16%)
Abdominal pain				< 0.001
N-Miss	3	0	3
No	236 (78%)	25 (47%)	261 (73%)
Yes	39 (13%)	2 (4%)	41 (12%)
Unknown	28 (9%)	26 (49%)	54 (15%)
Altered mental status on presentation				< 0.001
N-Miss	1	0	1
No	245 (80%)	18 (34%)	263 (73%)
Yes	58 (19%)	35 (66%)	93 (26%)
Unknown	2 (1%)	0 (0%)	2 (1%)

Initial Values

summary(tableby(died ~ temp_max + temp_min + hr_max + hr_min + sbp_min + max_map + 
    min_map + rr_max + rr_min + sao2_min + sao2_fio2 + dopamine_sofa + dobutamine_sofa + 
    epinepherine_sofa + norepinephrine_sofa + na + k + creatinine + alt + ast + tbili + 
    lactate + wbc + hgb + hct + platelets + lymphocytes + abg_pao2 + abg_paco2 + 
    abg_spo2 + abg_ph, data = covid1, control = mytables))

	Alive (N=306)	Died (N=53)	Total (N=359)	p value
Temperature maximum, °C				0.905
N-Miss	1	1	2
Mean (SD)	38 (4)	38 (1)	38 (4)
Range	15 - 101	36 - 40	15 - 101
Temperature minimum, °C				0.277
N-Miss	1	1	2
Mean (SD)	37 (4)	36 (1)	37 (3)
Range	34 - 99	33 - 38	33 - 99
Heart rate maximum, bpm				0.003
N-Miss	1	1	2
Mean (SD)	101 (19)	110 (18)	103 (19)
Range	36 - 168	73 - 158	36 - 168
Heart rate minimum, bpm				0.288
N-Miss	1	1	2
Mean (SD)	75 (15)	77 (13)	75 (14)
Range	41 - 127	44 - 106	41 - 127
Systolic blood pressure minimum, mm Hg				< 0.001
N-Miss	1	1	2
Mean (SD)	112 (18)	97 (20)	110 (19)
Range	63 - 180	32 - 146	32 - 180
Mean arterial pressure maximum, mm Hg				0.035
N-Miss	2	1	3
Mean (SD)	107 (17)	112 (19)	108 (17)
Range	65 - 166	83 - 196	65 - 196
Mean arterial pressure minimum, mm Hg				< 0.001
N-Miss	2	1	3
Mean (SD)	78 (13)	69 (15)	77 (14)
Range	42 - 136	27 - 98	27 - 136
Respiratory rate maximum, bpm				< 0.001
N-Miss	1	1	2
Mean (SD)	26 (7)	31 (8)	26 (8)
Range	11 - 54	12 - 51	11 - 54
Respiratory rate minimum, bpm				< 0.001
N-Miss	1	1	2
Mean (SD)	15 (3)	18 (8)	16 (4)
Range	5 - 34	9 - 65	5 - 65
Oxygen saturation minimum, %				< 0.001
N-Miss	1	1	2
Mean (SD)	93 (5)	88 (7)	92 (6)
Range	55 - 100	58 - 97	55 - 100
FiO2 at saturation minimum, decimal				< 0.001
N-Miss	4	1	5
Mean (SD)	0 (0)	1 (0)	0 (0)
Range	0 - 1	0 - 1	0 - 1
Dopamine				< 0.001
N-Miss	3	1	4
No	303 (100%)	52 (100%)	355 (100%)
Dobutamine				< 0.001
N-Miss	2	1	3
No	304 (100%)	52 (100%)	356 (100%)
Epinepherine				< 0.001
N-Miss	2	0	2
No	303 (100%)	49 (92%)	352 (99%)
Yes	1 (0%)	4 (8%)	5 (1%)
Norepinephrine				< 0.001
N-Miss	2	0	2
No	296 (97%)	42 (79%)	338 (95%)
Yes	8 (3%)	11 (21%)	19 (5%)
Initial Sodium, meql/L				< 0.001
N-Miss	17	1	18
Mean (SD)	138 (6)	144 (7)	139 (6)
Range	119 - 168	134 - 158	119 - 168
Initial Potassium, meq/L				0.003
N-Miss	17	1	18
Mean (SD)	4 (1)	4 (1)	4 (1)
Range	2 - 8	3 - 8	2 - 8
Initial Creatinine, mg/dL				0.081
N-Miss	17	1	18
Mean (SD)	2 (3)	3 (3)	2 (3)
Range	0 - 15	0 - 18	0 - 18
ALT initial, IU/L				< 0.001
N-Miss	20	2	22
Mean (SD)	31 (37)	56 (64)	35 (43)
Range	3 - 452	7 - 310	3 - 452
AST initial, IU/L				< 0.001
N-Miss	20	2	22
Mean (SD)	42 (77)	92 (108)	49 (84)
Range	5 - 1197	15 - 578	5 - 1197
Total bilirubin initial, mg/dL				0.400
N-Miss	20	2	22
Mean (SD)	1 (0)	1 (0)	1 (0)
Range	0 - 3	0 - 2	0 - 3
Lactate initial, mmol/L				< 0.001
N-Miss	161	6	167
Mean (SD)	2 (1)	4 (3)	3 (2)
Range	0 - 10	1 - 15	0 - 15
WBC count initial, K/mcL				0.022
N-Miss	14	1	15
Mean (SD)	7 (3)	8 (4)	7 (3)
Range	1 - 28	2 - 19	1 - 28
Hemoglobin initial, g/dL				0.698
N-Miss	14	1	15
Mean (SD)	13 (2)	12 (3)	13 (2)
Range	5 - 17	6 - 17	5 - 17
Hematocrit initial, %				0.613
N-Miss	14	1	15
Mean (SD)	38 (7)	39 (8)	38 (7)
Range	10 - 54	17 - 53	10 - 54
Platelet Count initial, K/mcL				0.673
N-Miss	14	1	15
Mean (SD)	233 (93)	227 (114)	232 (97)
Range	13 - 675	55 - 742	13 - 742
Lymphocytes initial, K/mcL				0.213
N-Miss	22	2	24
Mean (SD)	1 (1)	1 (2)	1 (1)
Range	0 - 5	0 - 10	0 - 10
PA02 initial, mm Hg				0.286
N-Miss	263	25	288
Mean (SD)	104 (48)	120 (77)	110 (61)
Range	33 - 234	32 - 346	32 - 346
PAC02 initial, mm Hg				0.846
N-Miss	263	25	288
Mean (SD)	38 (9)	39 (15)	38 (12)
Range	20 - 68	21 - 92	20 - 92
Arterial SpO2 initial, %				0.419
N-Miss	264	27	291
Mean (SD)	93 (9)	91 (12)	93 (10)
Range	50 - 99	38 - 99	38 - 99
pH initial				0.300
N-Miss	263	25	288
Mean (SD)	7 (0)	7 (0)	7 (0)
Range	7 - 8	7 - 8	7 - 8

Extreme Values

summary(tableby(died ~ max_creat + cpk_max + troponin_max + bnp_max + crp_max + ldh_max + 
    hgb_a1c_max + ferritin_max + alt_max + ast_max + lactate_max + min_lymp + ddimer_max, 
    data = covid1, control = mytables))

	Alive (N=306)	Died (N=53)	Total (N=359)	p value
Creatinine maximum, mg/dL				0.002
N-Miss	17	1	18
Mean (SD)	2 (3)	4 (3)	2 (3)
Range	0 - 18	1 - 18	0 - 18
CPK maximum, U/L				0.899
N-Miss	222	14	236
Mean (SD)	1213 (5502)	1329 (2046)	1250 (4680)
Range	0 - 50000	0 - 8613	0 - 50000
Troponin-I maximum, ng/mL				0.087
N-Miss	114	6	120
Mean (SD)	0 (1)	1 (1)	0 (1)
Range	0 - 16	0 - 8	0 - 16
BNP maximum, pg/mL				0.307
N-Miss	186	14	200
Mean (SD)	442 (1246)	233 (396)	390 (1102)
Range	6 - 7906	14 - 2274	6 - 7906
HS-CRP maximum, mg/L				< 0.001
N-Miss	173	18	191
Mean (SD)	109 (88)	187 (66)	125 (89)
Range	0 - 240	0 - 240	0 - 240
Lactate dehydrogenase maximum, U/L				0.001
N-Miss	134	12	146
Mean (SD)	335 (187)	782 (1787)	421 (813)
Range	0 - 1518	237 - 11894	0 - 11894
Hemoglobin A1c maximum, %				0.568
N-Miss	191	30	221
Mean (SD)	7 (3)	7 (3)	7 (3)
Range	0 - 17	0 - 14	0 - 17
Ferritin maximum, ng/mL				0.004
N-Miss	185	20	205
Mean (SD)	945 (1553)	2304 (4169)	1236 (2417)
Range	0 - 9751	52 - 15000	0 - 15000
ALT maximum, IU/L				< 0.001
N-Miss	20	1	21
Mean (SD)	74 (451)	500 (1524)	140 (740)
Range	6 - 7500	8 - 7500	6 - 7500
AST maximum, IU/L				< 0.001
N-Miss	20	1	21
Mean (SD)	70 (187)	1058 (3258)	222 (1328)
Range	8 - 2585	19 - 16762	8 - 16762
Lactate maximum, mmol/L				< 0.001
N-Miss	156	2	158
Mean (SD)	2 (1)	6 (6)	3 (4)
Range	1 - 10	1 - 30	1 - 30
Lymphocytes minimum, K/mcL				0.042
N-Miss	21	2	23
Mean (SD)	1 (1)	1 (1)	1 (1)
Range	0 - 5	0 - 10	0 - 10
D-dimer maximum, ng/mL				< 0.001
N-Miss	154	8	162
Mean (SD)	5991 (16023)	22394 (37365)	9738 (23641)
Range	250 - 128000	471 - 128000	250 - 128000

Radiology

summary(tableby(died ~ cxr_done + cxr_abnormal + cxr_atelectasis + cxr_bilateral + 
    cxr_cavity + cxr_consolidation + cxr_interstital + cxr_mass + cxr_multifocal + 
    cxr_effusion + cxr_nodule + ct_done + us_done + vq_done + tte_done, data = covid1, 
    control = mytables))

	Alive (N=306)	Died (N=53)	Total (N=359)	p value
CXR performed				0.230
No	18 (6%)	1 (2%)	19 (5%)
Yes	288 (94%)	52 (98%)	340 (95%)
CXR abnormal				< 0.001
N-Miss	18	1	19
No	94 (33%)	4 (8%)	98 (29%)
Yes	194 (67%)	48 (92%)	242 (71%)
Atelectasis				0.683
N-Miss	18	1	19
No	202 (70%)	35 (67%)	237 (70%)
Yes	86 (30%)	17 (33%)	103 (30%)
Bilateral Opacities, not effusions				0.004
N-Miss	18	1	19
No	168 (58%)	19 (37%)	187 (55%)
Yes	120 (42%)	33 (63%)	153 (45%)
Cavity/abscess				< 0.001
N-Miss	18	1	19
No	288 (100%)	52 (100%)	340 (100%)
Consolidation				0.050
N-Miss	18	1	19
No	271 (94%)	45 (87%)	316 (93%)
Yes	17 (6%)	7 (13%)	24 (7%)
Interstitial				0.300
N-Miss	19	1	20
No	257 (90%)	44 (85%)	301 (89%)
Yes	30 (10%)	8 (15%)	38 (11%)
Mass/mass-like				0.018
N-Miss	18	1	19
No	288 (100%)	51 (98%)	339 (100%)
Yes	0 (0%)	1 (2%)	1 (0%)
Multifocal				0.018
N-Miss	18	1	19
No	248 (86%)	38 (73%)	286 (84%)
Yes	40 (14%)	14 (27%)	54 (16%)
Pleural effusion				< 0.001
N-Miss	19	1	20
No	273 (95%)	39 (75%)	312 (92%)
Yes	14 (5%)	13 (25%)	27 (8%)
Reticulonodular				0.933
N-Miss	21	1	22
No	280 (98%)	51 (98%)	331 (98%)
Yes	5 (2%)	1 (2%)	6 (2%)
Chest CT performed				0.152
No	237 (77%)	36 (68%)	273 (76%)
Yes, with IV contrast	50 (16%)	10 (19%)	60 (17%)
Yes, without IV contrast	19 (6%)	7 (13%)	26 (7%)
Ultrasound of extremities performed				< 0.001
N-Miss	1	0	1
No	268 (88%)	35 (66%)	303 (85%)
Yes	37 (12%)	18 (34%)	55 (15%)
VQ scan performed				< 0.001
N-Miss	0	1	1
No	306 (100%)	52 (100%)	358 (100%)
TTE performed				< 0.001
N-Miss	1	0	1
No	242 (79%)	28 (53%)	270 (75%)
Yes	63 (21%)	25 (47%)	88 (25%)

Microbiology

summary(tableby(died ~ sars_samples + blood_cx + resp_cx + procal_72 + influenza + 
    legionella + hiv_test_alt + hiv_vl + cd4, data = covid1, control = mytables))

	Alive (N=306)	Died (N=53)	Total (N=359)	p value
Number of SARS-CoV-2 tests				0.802
N-Miss	1	0	1
Mean (SD)	2 (2)	2 (1)	2 (2)
Range	0 - 15	1 - 7	0 - 15
Blood cultures in first 48 hours				< 0.001
N-Miss	2	0	2
Negative	118 (39%)	38 (72%)	156 (44%)
Not done	174 (57%)	7 (13%)	181 (51%)
Positive	12 (4%)	8 (15%)	20 (6%)
Respiratory cultures in first 48 hours				< 0.001
N-Miss	3	0	3
Negative	21 (7%)	7 (13%)	28 (8%)
Not done	279 (92%)	41 (77%)	320 (90%)
Positive	3 (1%)	5 (9%)	8 (2%)
Procalcitonin initial (48h), ng/mL				0.803
N-Miss	179	16	195
Mean (SD)	3 (16)	3 (7)	3 (14)
Range	0 - 164	0 - 28	0 - 164
Influenza PCR				0.046
N-Miss	4	2	6
A/B+	81 (27%)	7 (14%)	88 (25%)
Not Done	221 (73%)	44 (86%)	265 (75%)
Legionella urine antigen				0.043
Negative	29 (9%)	10 (19%)	39 (11%)
Not done	277 (91%)	43 (81%)	320 (89%)
HIV test				0.042
N-Miss	1	0	1
Current or Prior negative < 1 year	132 (43%)	16 (30%)	148 (41%)
Current or Prior Positive	12 (4%)	0 (0%)	12 (3%)
Not done, no prior	161 (53%)	37 (70%)	198 (55%)
Viral load undetectable
N-Miss	294	53	347
No	6 (50%)	0	6 (50%)
Yes	5 (42%)	0	5 (42%)
Not done	1 (8%)	0	1 (8%)
CD4, K/L
N-Miss	296	53	349
Mean (SD)	314 (269)	NA	314 (269)
Range	75 - 874	NA	75 - 874

Drugs

summary(tableby(died ~ covid_tx___1 + covid_tx___2 + covid_tx___3 + covid_tx___4 + 
    covid_tx___5 + covid_tx___0 + abx_received___1 + abx_received___2 + abx_received___3 + 
    abx_received___4 + abx_received___5 + abx_received___6 + abx_received___7 + abx_other + 
    steroid + anticoagulation, data = covid1, control = mytables))

	Alive (N=306)	Died (N=53)	Total (N=359)	p value
Redesmivir, on clinical trial				0.014
No	298 (97%)	48 (91%)	346 (96%)
Yes	8 (3%)	5 (9%)	13 (4%)
Redesmivir, not on clinical trial				0.677
No	305 (100%)	53 (100%)	358 (100%)
Yes	1 (0%)	0 (0%)	1 (0%)
Lopinavir/ritonavir				0.677
No	305 (100%)	53 (100%)	358 (100%)
Yes	1 (0%)	0 (0%)	1 (0%)
Hydroxycloroquine				0.363
No	268 (88%)	44 (83%)	312 (87%)
Yes	38 (12%)	9 (17%)	47 (13%)
Tocilizumab				0.159
No	305 (100%)	52 (98%)	357 (99%)
Yes	1 (0%)	1 (2%)	2 (1%)
No SARS-CoV-2 Therapies				0.016
Mean (SD)	1 (0)	1 (0)	1 (0)
Range	0 - 1	0 - 1	0 - 1
Azithromycin				0.018
No	207 (68%)	27 (51%)	234 (65%)
Yes	99 (32%)	26 (49%)	125 (35%)
Ceftriaxone				0.060
No	208 (68%)	29 (55%)	237 (66%)
Yes	98 (32%)	24 (45%)	122 (34%)
Doxycycline				0.297
No	293 (96%)	49 (92%)	342 (95%)
Yes	13 (4%)	4 (8%)	17 (5%)
ESBLactam				< 0.001
No	261 (85%)	24 (45%)	285 (79%)
Yes	45 (15%)	29 (55%)	74 (21%)
Fluoroquinolone				0.562
No	303 (99%)	52 (98%)	355 (99%)
Yes	3 (1%)	1 (2%)	4 (1%)
Vancomycin				< 0.001
No	256 (84%)	23 (43%)	279 (78%)
Yes	50 (16%)	30 (57%)	80 (22%)
Other Antibiotic				0.505
No	280 (92%)	47 (89%)	327 (91%)
Yes	26 (8%)	6 (11%)	32 (9%)
Other Antibiotics description				0.360
N-Miss	282	47	329
Acyclovir	1 (4%)	0 (0%)	1 (3%)
acyclovir (home med)	1 (4%)	0 (0%)	1 (3%)
amoxicillin	1 (4%)	0 (0%)	1 (3%)
Amoxicillin-clavulanate	1 (4%)	0 (0%)	1 (3%)
ampicillin	1 (4%)	0 (0%)	1 (3%)
Augmentin	3 (12%)	0 (0%)	3 (10%)
aztreonam	1 (4%)	0 (0%)	1 (3%)
Bactrim	1 (4%)	0 (0%)	1 (3%)
Bactrim (HIV prophylactic dose)	1 (4%)	0 (0%)	1 (3%)
Cefazolin	1 (4%)	1 (17%)	2 (7%)
cefazolin (peri-op)	1 (4%)	0 (0%)	1 (3%)
cefazolin, cefoxitin	1 (4%)	0 (0%)	1 (3%)
Cefepime	1 (4%)	0 (0%)	1 (3%)
Cefuroxime	1 (4%)	0 (0%)	1 (3%)
Dapsone	1 (4%)	0 (0%)	1 (3%)
flagyl (empiric treatment for STIs, pyuria)	1 (4%)	0 (0%)	1 (3%)
Linezolid, clindamycin, Tobramycin	0 (0%)	1 (17%)	1 (3%)
metronidazole	4 (17%)	0 (0%)	4 (13%)
Metronidazole	1 (4%)	1 (17%)	2 (7%)
Nitrofurantoin	1 (4%)	0 (0%)	1 (3%)
tobramycin	0 (0%)	1 (17%)	1 (3%)
Tobramycin	0 (0%)	1 (17%)	1 (3%)
tobramycin, aztrenam	0 (0%)	1 (17%)	1 (3%)
Steroid Combined				< 0.001
N-Miss	3	2	5
No	277 (91%)	35 (69%)	312 (88%)
Yes	26 (9%)	16 (31%)	42 (12%)
Therapeutic anticoagulation				< 0.001
No	247 (81%)	30 (57%)	277 (77%)
Yes, chronic	22 (7%)	3 (6%)	25 (7%)
Yes, new	37 (12%)	20 (38%)	57 (16%)

Other Tx & Dispo

summary(tableby(died ~ patient_location + admit_loc + admit_service + fu_loc + hosp_los + 
    icu_los + hfnc + niv + ett + extubated + rrt + arrest + discharge_hai + discharge_disposition + 
    discharge_o2 + discharge_hd, data = covid1, control = mytables))

	Alive (N=306)	Died (N=53)	Total (N=359)	p value
SARS-CoV-2 Location				< 0.001
ECC/Hospital	306 (100%)	53 (100%)	359 (100%)
Admission Level of Care				< 0.001
Discharged from ED	67 (22%)	2 (4%)	69 (19%)
General	184 (60%)	20 (38%)	204 (57%)
Intensive	21 (7%)	21 (40%)	42 (12%)
Intermediate	34 (11%)	10 (19%)	44 (12%)
Admission Service				0.013
N-Miss	1	0	1
ECC/Hospital	67 (22%)	2 (4%)	69 (19%)
Medical	219 (72%)	50 (94%)	269 (75%)
Neurology	1 (0%)	0 (0%)	1 (0%)
Other	7 (2%)	0 (0%)	7 (2%)
Surgical	11 (4%)	1 (2%)	12 (3%)
Highest Level of Care				< 0.001
N-Miss	1	1	2
ECC	67 (22%)	2 (4%)	69 (19%)
General	168 (55%)	8 (15%)	176 (49%)
Intensive	34 (11%)	35 (67%)	69 (19%)
Intermediate	36 (12%)	7 (13%)	43 (12%)
Hospital LOS				0.377
N-Miss	2	1	3
Mean (SD)	9 (14)	11 (8)	9 (13)
Range	0 - 123	0 - 37	0 - 123
ICU LOS				0.359
N-Miss	272	18	290
Mean (SD)	14 (15)	113 (622)	64 (443)
Range	1 - 61	0 - 3688	0 - 3688
High flow nasal cannula				< 0.001
N-Miss	2	0	2
No	282 (93%)	29 (55%)	311 (87%)
Yes	22 (7%)	24 (45%)	46 (13%)
Non-invasive ventilation				0.200
N-Miss	2	0	2
Mean (SD)	0 (0)	0 (0)	0 (0)
Range	0 - 1	0 - 1	0 - 1
Endotracheal intubation				< 0.001
No	280 (92%)	23 (43%)	303 (84%)
Yes	26 (8%)	30 (57%)	56 (16%)
Disposition of ETT				< 0.001
N-Miss	0	3	3
Discharged with ETT	1 (0%)	1 (2%)	2 (1%)
Extubated < 24 hours before death/hospice	0 (0%)	5 (10%)	5 (1%)
No ETT	280 (92%)	23 (46%)	303 (85%)
Other	0 (0%)	1 (2%)	1 (0%)
Present at death	0 (0%)	17 (34%)	17 (5%)
Removed for tracheostomy	4 (1%)	0 (0%)	4 (1%)
To spontaneous breathing	21 (7%)	3 (6%)	24 (7%)
Renal replacement therapy				< 0.001
N-Miss	1	0	1
Continuous	4 (1%)	6 (11%)	10 (3%)
Intermittent	11 (4%)	3 (6%)	14 (4%)
No	290 (95%)	44 (83%)	334 (93%)
Cardiac Arrest				< 0.001
N-Miss	1	0	1
No	301 (99%)	38 (72%)	339 (95%)
Yes	4 (1%)	15 (28%)	19 (5%)
Hospital-Acquired Infection				0.896
N-Miss	1	1	2
No	279 (91%)	48 (92%)	327 (92%)
Yes	16 (5%)	2 (4%)	18 (5%)
Unknown	10 (3%)	2 (4%)	12 (3%)
Discharge disposition detail				< 0.001
AMA	4 (1%)	0 (0%)	4 (1%)
Died	0 (0%)	31 (58%)	31 (9%)
Home	231 (75%)	0 (0%)	231 (64%)
Hospice	0 (0%)	22 (42%)	22 (6%)
Long-term Acute Care (LTAC)	3 (1%)	0 (0%)	3 (1%)
Nursing Home/Rehabilitation Facility	64 (21%)	0 (0%)	64 (18%)
Transfer to the hospital	4 (1%)	0 (0%)	4 (1%)
New or increased oxygen requirement at discharge/death				< 0.001
N-Miss	1	0	1
No	291 (95%)	5 (9%)	296 (83%)
Yes	13 (4%)	46 (87%)	59 (16%)
Unknown	1 (0%)	2 (4%)	3 (1%)
New HD requirement at discharge/death				< 0.001
N-Miss	1	0	1
No	304 (100%)	47 (89%)	351 (98%)
Yes	1 (0%)	6 (11%)	7 (2%)

Exploratory Figures for Markers of Mortality

Age

mort_na = glm(died.numeric ~ age, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$age), mort_na$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Age")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

na

mort_na = glm(died.numeric ~ na, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$na), mort_na$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial na")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

k

mort_k = glm(died.numeric ~ k, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$k), mort_k$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial k")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

max_creat

mort_max_creat = glm(died.numeric ~ max_creat, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$max_creat), mort_max_creat$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "max_creat")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

troponin_max

mort_troponin_max = glm(died.numeric ~ troponin_max, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$troponin_max), mort_troponin_max$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "troponin_max")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

bnp_max

mort_bnp_max = glm(died.numeric ~ bnp_max, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$bnp_max), mort_bnp_max$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "bnp_max")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ldh_max

mort_ldh_max = glm(died.numeric ~ ldh_max, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$ldh_max), mort_ldh_max$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "ldh_max")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

hgb_a1c_max

mort_hgb_a1c_max = glm(died.numeric ~ hgb_a1c_max, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$hgb_a1c_max), mort_hgb_a1c_max$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "hgb_a1c_max")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ferritin_max

mort_ferritin_max = glm(died.numeric ~ ferritin_max, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$ferritin_max), mort_ferritin_max$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "ferritin_max")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

AST

mort_ast = glm(died.numeric ~ ast, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$ast), mort_ast$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial AST")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ALT

mort_alt = glm(died.numeric ~ alt, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$alt), mort_alt$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial alt")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

alt_max

mort_alt_max = glm(died.numeric ~ alt_max, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$alt_max), mort_alt_max$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "alt_max")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ast_max

mort_ast_max = glm(died.numeric ~ ast_max, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$ast_max), mort_ast_max$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "ast_max")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

tbili

mort_tbili = glm(died.numeric ~ tbili, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$tbili), mort_tbili$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial tbili")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

creatinine

mort_creatinine = glm(died.numeric ~ creatinine, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$creatinine), mort_creatinine$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial creatinine")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Lactate

mort_lactate = glm(died.numeric ~ lactate, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$lactate), mort_lactate$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial Lactate")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

lactate_max

mort_lactate_max = glm(died.numeric ~ lactate_max, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$lactate_max), mort_lactate_max$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "lactate_max")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

hgb

mort_hgb = glm(died.numeric ~ hgb, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$hgb), mort_hgb$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial hgb")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

hct

mort_hct = glm(died.numeric ~ hct, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$hct), mort_hct$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial hct")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

WBC

mort_wbc = glm(died.numeric ~ wbc, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$wbc), mort_wbc$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial wbc")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Lymphs

mort_lymphocytes = glm(died.numeric ~ lymphocytes, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$lymphocytes), mort_lymphocytes$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial lymphocytes")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

min_lymp

mort_min_lymp = glm(died.numeric ~ min_lymp, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$min_lymp), mort_min_lymp$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "min_lymp")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Procalcitonin

mort_procal_72 = glm(died.numeric ~ procal_72, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$procal_72), mort_procal_72$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial procal_72")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

CRP

mort_crp_max = glm(died.numeric ~ crp_max, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$crp_max), mort_crp_max$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "max crp_max")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

DDimer

mort_ddimer_max = glm(died.numeric ~ ddimer_max, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$ddimer_max), mort_ddimer_max$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "max ddimer_max")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Exploratory Figures for Markers of Mortality, Age-Adjusted

na

mort_na = glm(died.numeric ~ age + na, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$na), mort_na$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial na")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

k

mort_k = glm(died.numeric ~ age + k, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$k), mort_k$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial k")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

max_creat

mort_max_creat = glm(died.numeric ~ age + max_creat, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$max_creat), mort_max_creat$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "max_creat")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

troponin_max

mort_troponin_max = glm(died.numeric ~ age + troponin_max, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$troponin_max), mort_troponin_max$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "troponin_max")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

bnp_max

mort_bnp_max = glm(died.numeric ~ age + bnp_max, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$bnp_max), mort_bnp_max$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "bnp_max")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ldh_max

mort_ldh_max = glm(died.numeric ~ age + ldh_max, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$ldh_max), mort_ldh_max$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "ldh_max")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

hgb_a1c_max

mort_hgb_a1c_max = glm(died.numeric ~ age + hgb_a1c_max, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$hgb_a1c_max), mort_hgb_a1c_max$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "hgb_a1c_max")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ferritin_max

mort_ferritin_max = glm(died.numeric ~ age + ferritin_max, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$ferritin_max), mort_ferritin_max$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "ferritin_max")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

AST

mort_ast = glm(died.numeric ~ age + ast, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$ast), mort_ast$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial AST")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ALT

mort_alt = glm(died.numeric ~ age + alt, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$alt), mort_alt$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial alt")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

alt_max

mort_alt_max = glm(died.numeric ~ age + alt_max, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$alt_max), mort_alt_max$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "alt_max")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ast_max

mort_ast_max = glm(died.numeric ~ age + ast_max, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$ast_max), mort_ast_max$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "ast_max")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

tbili

mort_tbili = glm(died.numeric ~ age + tbili, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$tbili), mort_tbili$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial tbili")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

creatinine

mort_creatinine = glm(died.numeric ~ age + creatinine, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$creatinine), mort_creatinine$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial creatinine")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Lactate

mort_lactate = glm(died.numeric ~ age + lactate, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$lactate), mort_lactate$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial Lactate")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

lactate_max

mort_lactate_max = glm(died.numeric ~ age + lactate_max, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$lactate_max), mort_lactate_max$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "lactate_max")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

hgb

mort_hgb = glm(died.numeric ~ age + hgb, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$hgb), mort_hgb$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial hgb")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

hct

mort_hct = glm(died.numeric ~ age + hct, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$hct), mort_hct$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial hct")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

WBC

mort_wbc = glm(died.numeric ~ age + wbc, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$wbc), mort_wbc$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial wbc")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Lymphs

mort_lymphocytes = glm(died.numeric ~ age + lymphocytes, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$lymphocytes), mort_lymphocytes$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial lymphocytes")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

min_lymp

mort_min_lymp = glm(died.numeric ~ age + min_lymp, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$min_lymp), mort_min_lymp$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "min_lymp")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Procalcitonin

mort_procal_72 = glm(died.numeric ~ age + procal_72, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$procal_72), mort_procal_72$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "Initial procal_72")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

CRP

mort_crp_max = glm(died.numeric ~ age + crp_max, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$crp_max), mort_crp_max$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "max crp_max")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

DDimer

mort_ddimer_max = glm(died.numeric ~ age + ddimer_max, data = covid1, family = "binomial")
data = as.data.frame(cbind(na.omit(covid1$ddimer_max), mort_ddimer_max$fitted))
ggplot(data = data, aes(x = V1, y = V2)) + geom_point() + geom_smooth() + theme_bw() + 
    labs(y = "Probability of Hospital Mortality/Hospice", x = "max ddimer_max")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

COVID Case Series

COVID Study Group

2020-10-29

Introduction

Cohort Selection

Figures for all COVID Tests

Daily Total

Daily Cumulative

Daily Percent

Weekly Total

Weekly Cumulative

Weekly Percent

Tables for Mortality

Demographics

Comorbidity

Symptoms

Initial Values

Extreme Values

Radiology

Microbiology

Drugs

Other Tx & Dispo

Exploratory Figures for Markers of Mortality

Age

na

k

max_creat

troponin_max

bnp_max

ldh_max

hgb_a1c_max

ferritin_max

AST

ALT

alt_max

ast_max

tbili

creatinine

Lactate

lactate_max

hgb

hct

WBC

Lymphs

min_lymp

Procalcitonin

CRP

DDimer

Exploratory Figures for Markers of Mortality, Age-Adjusted

na

k

max_creat

troponin_max

bnp_max

ldh_max

hgb_a1c_max

ferritin_max

AST

ALT

alt_max

ast_max

tbili

creatinine

Lactate

lactate_max

hgb

hct

WBC

Lymphs

min_lymp

Procalcitonin

CRP

DDimer