Electronic health record analysis identifies kidney disease as the leading risk factor for hospitalization in confirmed COVID-19 patients.

BACKGROUND: Empirical data on conditions that increase risk of coronavirus disease 2019 (COVID-19) progression are needed to identify high risk individuals. We performed a comprehensive quantitative assessment of pre-existing clinical phenotypes associated with COVID-19-related hospitalization.
METHODS: Phenome-wide association study (PheWAS) of SARS-CoV-2-positive patients from an integrated health system (Geisinger) with system-level outpatient/inpatient COVID-19 testing capacity and retrospective electronic health record (EHR) data to assess pre-COVID-19 pandemic clinical phenotypes associated with hospital admission (hospitalization).
RESULTS: Of 12,971 individuals tested for SARS-CoV-2 with sufficient pre-COVID-19 pandemic EHR data at Geisinger, 1604 were SARS-CoV-2 positive and 354 required hospitalization. We identified 21 clinical phenotypes in 5 disease categories meeting phenome-wide significance (P<1.60x10-4), including: six kidney phenotypes, e.g. end stage renal disease or stage 5 CKD (OR = 11.07, p = 1.96x10-8), six cardiovascular phenotypes, e.g. congestive heart failure (OR = 3.8, p = 3.24x10-5), five respiratory phenotypes, e.g. chronic airway obstruction (OR = 2.54, p = 3.71x10-5), and three metabolic phenotypes, e.g. type 2 diabetes (OR = 1.80, p = 7.51x10-5). Additional analyses defining CKD based on estimated glomerular filtration rate, confirmed high risk of hospitalization associated with pre-existing stage 4 CKD (OR 2.90, 95% CI: 1.47, 5.74), stage 5 CKD/dialysis (OR 8.83, 95% CI: 2.76, 28.27), and kidney transplant (OR 14.98, 95% CI: 2.77, 80.8) but not stage 3 CKD (OR 1.03, 95% CI: 0.71, 1.48).
CONCLUSIONS: This study provides quantitative estimates of the contribution of pre-existing clinical phenotypes to COVID-19 hospitalization and highlights kidney disorders as the strongest factors associated with hospitalization in an integrated US healthcare system.

Introduction

Coronavirus disease 2019 (COVID-19) is an emerging illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. COVID-19 was declared a pandemic by the World Health Organization in March 2020. The United States reported the first case on January 22, 2020; by October 12th, there were >7,740,000 total cases and >214,000 deaths (cdc.gov). The severity of COVID-19 illness is variable, ranging from asymptomatic [1] to severe complications that require hospitalization [2]. Several pre-existing conditions have been identified as risk factors for COVID-19-related hospitalization and death [3, 4]. A recent study developed a risk score that predicted progression to intensive care in hospitalized patients based on present and preexisting risk factors (e.g. chest radiographic abnormality, hemoptysis, dyspnea, history of cancer and other comorbidities) [5]. Comprehensive quantitative data on the contribution of pre-existing conditions to COVID-19 disease severity are still needed. We applied an agnostic cross-disease approach [6] to data captured in the patient’s electronic health record (EHR) of SARS-COV-2-positive patients to identify associations between pre-existing conditions and COVID-19-related hospitalization.

Methods

This study was conducted at Geisinger, an integrated health system in central and northeastern Pennsylvania [7]. This study was reviewed and approved by the Geisinger Institutional Review Board. This analysis includes patients with a laboratory confirmed diagnosis of COVID-19 reported between March 7, 2020 and May 19, 2020. All patients displayed symptoms that met CDC screening criteria for COVID-19 at the time of testing.

International Classification of Diseases Ninth (ICD-9) and Tenth (ICD-10) revision disease diagnosis codes and the last outpatient serum creatinine value were extracted from patients’ EHR dated prior to January 1st, 2020. Potential risk factor phenotypes were defined by PheCodes mapped from ICD codes using PheCodes Map 1.2 [8] (https://phewascatalog.org/phecodes). For each individual, duplicate PheCode occurrences on the same date were dropped such that only one occurrence per date for a given PheCode remained. Cases for a phenotype were defined as having at least three occurrences of the PheCode; individuals with one or two occurrences were excluded from analysis of the phenotype, and the remaining individuals were classified as controls. To ensure that individuals in the study were adequately assessed for clinical history during clinical care, we restricted the analyses to individuals who were cases for at least one phenotype, which denotes that they have been clinically assessed on at least three distinct occasions. Our analysis required at least 20 cases and 20 controls for each phenotype among the 1,604 SARS-CoV-2 positive subjects, resulting in 313 distinct phenotypes. S1 Fig shows a flow diagram of the study design. The ICD code terminology used for the PheWAS data reflects the codes utilized by the PheCode Map 1.2 exactly.

We conducted additional analyses to further explore the relationship between kidney diseases and risk of COVID-19 hospitalization. We used estimated glomerular filtration rate (eGFR), calculated by the CKD Epidemiology Collaboration equation, data up until August 2018 from the United States Renal Data System (USRDS [9] https://www.usrds.org/2018,), and ICD codes to categorize patients into 1 of 5 groups: 1) eGFR ≥ 60 ml/min/1.73m2 without kidney transplant; 2) eGFR 30–59 ml/min/1.73m2 without kidney transplant; 3) eGFR 15–29 ml/min/1.73m2 without kidney transplant; 4) eGFR <15 ml/min/1.73m2 or on dialysis; 5) kidney transplant with eGFR ≥ 15 ml/min/1.73m2.

Statistics

A phenome-wide association study (PheWAS) was performed to identify pre-existing conditions associated with hospitalization of patients with SARS-COV-2 infection. Tests were performed with Firth’s logistic regression [8] adjusted for age, sex and race:

Odds ratios (ORs) indicate the relative odds of COVID-19 related hospital admission given the presence of a pre-existing phenotype. We defined phenome-wide significance using a Bonferroni corrected p-value for the number of clinical PheCodes tested (p<0.05/313 = 1.60x10-4).

Results

Of 18,372 individuals tested for SARS-CoV-2 at Geisinger between March 7, 2020 and May 19, 2020; 15,707 tested negative, 2,665 tested positive, and 565 were admitted to the hospital. Among the total number tested, 12,971 met inclusion criteria for PheWAS analysis (Methods). Of the 12,971 SARS-CoV-2 tested patients used in PheWAS, 1,604 were positive for SARS-CoV-2 of whom 354 (22.1%) were admitted to the hospital (Table 1; demographics). Admitted patients were more likely to be older and male (p < 0.0001, Table 1). Of the 354 hospitalized patients, 106 were admitted to the ICU, 70 required ventilation, 71 died, and 54 remained hospitalized as of May 19, 2020.

Table 1

Patient demographics and prevalence of select chronic conditions derived from EHR.

Inclusion Criteria	Population	SARS-CoV-2 tested	SARS-CoV-2 negative	SARS-CoV-2 positive	Admitted	p value
N	1,069,142	12,971	11,367	1,250	354
Age, mean (SD)	49.1 (25.4)	49.2 (20.6)	48.0 (20.2)	54.8 (21.0)	66.8 (17.3)	3.88x10-22
Male, %	45.9	37.1	36.8	36.4	50.3	3.22x10-6
BMI (SD)	27.5 (8.2)	30.2 (8.2)	30.0 (8.3)	30.8 (7.7)	31.4 (8.1)	0.229
Current Smokers, %	15.7	22.3	24.2	10.0	6.8	0.082
Former Smokers, %	24.9	32.4	31.9	32.7	47.2	7.73x10-7
Chronic kidney disease, %	7.6	10.9	10.4	9.2	29.7	3.30x10-19
Chronic lung disease, %	5.5	10.7	11.2	5.0	16.9	1.38x10-13
Diabetes mellitus, %	16.1	23.1	22.3	23.9	45.5	4.11x10-15
Heart Failure, %	5.1	7.4	7.2	5.5	17.8	2.66x10-13
Hypertension, %	30.2	38.1	37.1	38.5	63.8	6.84x10-14
History Pneumonia, %	3.5	9.6	9.4	5.8	27.7	3.49x10-16
Respiratory distress, %	0.9	1.5	1.6	0.9	2.0	0.149

(p-values refer to comparison between SARS-CoV-2 positive and admitted patients, using unpaired t-test for Age and BMI, chi-square test for others. EHR Inclusion is defined in the results).

We performed a PheWAS analysis to test for associations between COVID-19 related hospital admission and 313 clinical phenotypes (Fig 1; Table 2). Phenotypes that reached phenome-wide significance (p < 1.60x10-4) fell into five disease categories: renal, cardiovascular, endocrine/metabolic, respiratory, and hematopoietic. The most significant associations (smallest p value and largest OR) were related to disorders of renal function, including chronic kidney disease (unspecified stage) (OR = 3.43, 95% CI [2.36,5], p = 1.33 x 10−10), end stage renal disease or stage 5 CKD (OR = 11.07, 95% CI [4.54,26.97], p = 1.96 x 10−8), stage III chronic kidney disease, (OR = 2.68, 95% CI [1.76,4.06], p = 4.74 x 10−6) and acute renal failure (OR = 3.26, 95% CI [1.89,5.62], p = 3.08 x 10−5). Six disorders in the cardiovascular disease category, including nonhypertensive congestive heart failure (OR = 3.35, 95% CI [2.16,5.2], p = 8.13 x 10−8), and peripheral vascular disease (OR = 3.25, 95% CI [1.84,5.71], p = 6.37 x 10−5) reached phenome-wide significance. Type 2 diabetes (OR = 1.8, 95% CI [1.35,2.41], p = 7.51 x 10−5) was among three disorders in the endocrine/metabolic disease category that reached phenome-wide significance. Within the respiratory disease category, 5 conditions were significant, including chronic airway obstruction (OR = 2.54, 95% CI [1.65,3.93], p = 3.71 x 10−5), pneumonia (OR = 3.17, 95% CI [1.89,5.33], p = 2.48 x 10−5), and chronic bronchitis (OR = 5.9, 95% CI [2.58,13.48], p = 3.26 x 10−5). Lastly, we identified a single hematopoietic association with anemia of chronic disease (OR = 4.86, 95% CI [2.33,10.15], p = 4.36 x 10−5). A list of all 313 conditions tested in PheWAS is shown in S1 Table.

Fig 1

Manhattan plot for clinical phenotypes associated with COVID-19 hospitalization.

Using a minimum case count of 20, we identified 313 clinical phenotypes, from PheCode Map 1.2, that could be used for these association studies. Dashed line denotes the Bonferroni significance (1.60X10-4).

Table 2

Summary data for clinical phenotypes significantly associated with COVID-19 associated hospitalization adjusted for age, sex and race.

PheCode*	Description	OR	p	Cases
585.3	Chronic kidney disease (Unspecified stage)	3.43	1.33 x 10−10	172
585.34	Chronic Kidney Disease, Stage IV	11.85	1.59 x 10−9	31
585	Acute renal failure (only includes ICD9 584)	2.95	2.43 x 10−9	204
585.32	End stage renal disease or stage 5 CKD	11.07	1.96 x 10−8	28
428	Congestive heart failure; nonhypertensive	3.35	8.13 x 10−8	104
585.33	Chronic Kidney Disease, Stage III	2.68	4.74 x 10−6	135
401.2	Hypertensive heart and kidney disease	2.99	9.54 x 10−6	141
428.3	Heart failure with reduced EF (HFrEF)	4.82	1.13 x 10−5	35
480	Pneumonia	3.17	2.48 x 10−5	66
585.1	Acute renal failure	3.26	3.08 x 10−5	63
428.1	Congestive heart failure (CHF), unspecified	3.8	3.24 x 10−5	45
496.2	Chronic bronchitis	5.9	3.26 x 10−5	24
496	Chronic airway obstruction, unspecified	2.54	3.71 x 10−5	101
285.2	Anemia of chronic disease	4.86	4.36 x 10−5	30
250	Diabetes mellitus	1.83	4.83 x 10−5	341
443.9	Peripheral vascular disease	3.25	6.37 x 10−5	53
428.4	Heart failure with preserved EF (HFpEF)	3.26	7.01 x 10−5	56
250.2	Type 2 diabetes	1.8	7.51 x 10−5	336
509.1	Respiratory failure	4.11	1.26 x 10−4	33
276.1	Electrolyte imbalance not elsewhere classified	2.69	1.53 x 10−4	76
509	Acute chest syndrome	3.69	1.59 x 10−4	38

*Each PheCode represents at least 1 and often several ICD-9 or ICD-10 codes. For list of ICD-9 codes included in each PheCode: https://phewascatalog.org/phecodes. For list of ICD-10 codes included in each PheCode: https://phewascatalog.org/phecodes_icd10cm.

In addition to PheWAS findings, we also used several algorithms that have been extensively validated to define disease phenotypes using EHR data (Table 1, S1 Methods). We observed significantly higher frequencies of several of these phenotypes in hospitalized patients; chronic kidney disease was most strongly associated with hospitalization (S2 Fig). In analyses using eGFR data and USRDS data, stage 4 CKD (OR 2.90, 95% CI: 1.47, 5.74), and stage 5 CKD/dialysis (OR 8.83, 95% CI: 2.76m 28.27) were associated with increased risk of COVID-19 hospitalization whereas stage 3 CKD was not (OR 1.03, 95% CI: 0.71, 1.48). Five (71%) out of 7 patients with history of kidney transplant were hospitalized (OR 14.98, 95% CI: 2.77, 80.88). Among 565 hospitalized patients, which included those who were not included in the PheWAS due to limited EHR data, 122 had some history of CKD. The metabolic burden among these 122 patients was higher than those without CKD. These patients were typically older and had significantly higher rates of death (Table 3).

Table 3

Clinical outcomes for hospitalized patients with known history of chronic kidney disease.

	CKD	non-CKD
Patient Count	122	443
Age (±SD)	74.8±11.67	61.75±17.67
BMI (±SD)	29.3±7.5	31.0±8.6
Diabetes	50.82% (62/122)	33.41% (148/443)
Hypertension	76.23% (93/122)	41.08% (182/443)
Heart Failure	40.16% (48/122)	6.55% (29/443)
Chronic Lung Disease	27.87% (34/122)	8.13% (36/443)
Ever Smoker	59.02% (72/122)	41.99% (186/443)
Days in Hospital (±SD))	7.5±5.9	7.4±6.6
Admitted to ICU	27.8% (34/122)	30.7% (136/443)
Days in ICU (±SD)	7.5±8.6	7.6±7.0
Ventilator used	16.4% (20/122)	20.3% (90/443)
Days on Ventilator (±SD)	10.0±8.70	8.3±6.9
Min Resp Rate (±SD)	13.2±3.9	13.5±4.2
SPO2 at admission (±SD)	95.0±6.4	93.9±6.5
AVG SPO2 (±SD)	94.8±4.0	95.3±2.3
Died	25.4% (31/122)	13.3% (59/443)

Among all 565 COVID-19 patients admitted to hospital, 122 had a history of CKD. Patients with known history of CKD were older, typically had higher disease burden and higher death rate (Odds Ratio = 2.2; p = 0.002, Fisher’s exact test).

Discussion

The outbreak of COVID-19 has spurred unprecedented efforts to characterize biological and clinical aspects of the disease [10, 11]. COVID-19 is the first pandemic in the digital health age, which has allowed rapid epidemiologic studies [1, 13]. Data from government agencies such as Centers for Medicare & Medicaid Services (cms.gov/covid-19-data-snapshot-fact-sheet), encompass large cohorts but are mostly snapshots that lack granular data and rely heavily on claims and provider supplied data. Here, we used data from an integrated health system with outpatient and inpatient COVID-19 testing capacity and utilized a PheWAS study design to conduct a comprehensive analysis of clinical phenotypes associated with increased risk of COVID-19 related hospital admission. To control for potential bias related to exposure to the SARS-CoV-2 virus, we limited our study population to SARS-CoV-2 positive patients screened at Geisinger. Additional analyses using eGFR and USRDS data confirmed our findings that patients with stage 4–5 CKD, ESRD on dialysis or with kidney transplant are at extremely high risk for severe complications due to COVID-19 (Table 4). These findings complement findings from the OPEN Safely study, which found similar results but was limited by including clinically suspected (non laboratory confirmed) COVID-19 [12]. As well as the CMS reports showing higher risk of hospitalization among ESKD patients (not adjusted for covariates). Our finding of high risk of hospitalization in kidney transplant patients mirrors that of a case series of 36 consecutive kidney transplant patients at Montefiore where 28/36 (78%) were hospitalized [13]. The findings reported here identify co-morbidities that impact the clinical course of COVID-19 and may be used to identify individuals at greatest risk for COVID-19-related complications.

Table 4

Association between CKD phenotypes and COVID-19 associated hospitalization.

	Unadjusted	P value	Adj. for age, sex, race/ethnicity	P value
eGFR ≥ 60 (n = 1087)	Ref		Ref
eGFR 30–59 (n = 246)	1.76 (1.28, 2.42)	<0.001	1.03 (0.71, 1.48)	0.88
eGFR 15–30 (n = 38)	5.22 (2.73, 9.99)	<0.001	2.90 (1.47, 5.74)	0.002
eGFR <15 or on dialysis (n = 16)	13.43 (4.29, 42.08)	<0.001	8.83 (2.76, 28.27)	<0.001
Kidney transplant (n = 7)	11.36 (2.19, 59.00)	0.004	14.98 (2.77, 80.88)	0.002

Participants were classified into 1 of 5 groups based on their last eGFR before 1/1/2020, USRDS data, and ICD codes. Kidney transplant patients were classified separately and had a range of eGFR from 19.84 to 95.25 ml/min/1.73m2. N = 1394 for this analysis as eGFR was available for 1393 individuals; 1 patient with history of kidney transplant with no eGFR values before 1/1/2020 was included in this analysis.

The majority of conditions associated with increased risk of COVID-19 related hospital admission have been suggested in previous studies, including diabetes, heart failure, hypertension, and chronic kidney disease [14-16]. What is striking from our results is the magnitude of the kidney disease-related risk. Patients with end-stage renal disease were at 11-fold increased odds of hospitalization (Table 2). How clinical conditions increase the risk of COVID-19-related complications is not fully clear yet. The physiological stress caused by excessive inflammatory response to SARS-COV-2 infection could destabilize organs already weakened by chronic disease [17]. Alternatively, direct organ-specific injury from SARS-CoV-2 infection could act as a “second-hit” to these organs. Consistent with this hypothesis, kidney and heart are among the tissues with the highest expression of ACE2, a SARS-CoV-2 receptor [18].

The current study has several limitations. The sample size is relatively small, and the available data are limited to information captured in the EHR. Nevertheless, we were able to identify several highly significant traits associated with hospitalization, many of which are consistent with previous reports. The study population is subject to potential bias resulting from the availability of testing, which largely excluded asymptomatic individuals and an enrichment of individuals from nursing homes and healthcare workers [19]. To partially overcome this, we included in our analyses individuals who were tested in a single health system. This population is predominantly Caucasian, which may limit the generalization of our findings to other racial and ethnic groups. A recent study indicated that hospitalization rates may differ between racial and ethnic groups with COVID-19 [20].

In conclusion, this study leverages extensive longitudinal EHR data prior to the COVID-19 pandemic to identify pre-existing clinical phenotypes associated with increased risk of COVID-19 hospitalization. These results provide key information for public policymakers highlighting the need to prevent COVID-19 related illness in patients with kidney disease and other high-risk conditions.

A list of all 313 conditions tested in PheWAS.

(XLSX)

Click here for additional data file.

(PDF)

Click here for additional data file.

Study flow diagram.

(PDF)

Click here for additional data file.

Prevalence of validated disease phenotypes using EHR data among the total EHR population, all those tested for COVID-19, those who tested negative for COVID-19, COVID-19(+) individuals not needing admission and hospitalized for COVID-19(+) individuals.

(PDF)

Click here for additional data file.

11 Sep 2020

PONE-D-20-26257

Electronic health record analysis identifies kidney disease as the leading risk factor for hospitalization in confirmed COVID-19 patients.

PLOS ONE

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Reviewer #1: Authors pursue a very interesting and commendable endeavor of PheWAS of CoVID-19 positive patients to identify those at high risk for CoVID-related hospitalization. Following are our comments associated with each section:

Introduction

Line 6 regarding ‘Case reports have shown that older age, hypertension and diabetes are risk factors for COVID-19-related complications’. Would suggest omitting this statement (case reports) since there are a plethora of studies now available that demonstrate risk factors associated with CoVID-19 related complications. Would suggesting at least 2-3 major studies. For example, the study you quoted as reference [10].

Results

1. Second paragraph- would replace ‘acute renal failure’, with ‘acute kidney injury’ as terminology. Would be prudent to separate out pre-existing and new acute condition, since the intent is to find risk factors for CoVID related hospitalizations.

2. Also is the AKI at the time of diagnosis of CoVID-19 and how have you defined an AKI? The Methods specify ‘last outpatient serum creatinine value’, which could be very variable since most patients may not have had a creatinine checked proximal to diagnosis of CoVID-19. As per most recent data, AKIs in context of CoVID-19 have only developed during hospitalization course, rather than at the time of diagnosis. Please clarify your definition of AKI.

3. Second paragraph- how did you define ESKD as well, since you have mentioned CKD stage 5 and ESKD together. Please clarify.

4. Second paragraph- please explain what encompasses the term ‘nonhypertensive congestive heart failure’. A slightly non-descript term and might be confusing to the reader.

5. Second paragraph- you have listed pneumonia as a risk factor. Again, an acute event and is this also present at the time of diagnosis of CoVID-19.

6. On page 4 of the manuscript, there is a typo for number 15 following eGFR 15-29: 3) eGFR 15-29 15 ml/min/1.73m2 without kidney transplant;

Discussion

1. First paragraph- ‘However, initial reports of COVID-19 describing potential risk factors for hospitalization and other adverse outcomes have often been derived from cohorts without sufficient pre-hospitalization data that is often incomplete or missing’. Unfortunately, this is now an inaccurate statement- you could say that in March or April 2020. But as I quoted an example in the introduction section, there a good few studies now available looking at hospitalization risk and adverse outcome. Here’s another large study of >300,000 in PNAS (https://www.pnas.org/content/early/2020/08/10/2011086117). More- https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciaa1012/5872581,

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7245300/

Please consider updating the manuscript. Your study certainly does add to the literature of risk factors, and you could elucidate that in the discussion section.

2. First paragraph- ‘Further, confounders such as age, sex, and race are often not accounted for’. Again, would be inaccurate to state that as well. Please consider omitting, based on examples discussed above. Another example- https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-020-05144-x

https://www.nejm.org/doi/full/10.1056/NEJMsa2011686

3. First paragraph- ‘As well as the CMS reports showing higher risk of hospitalization among ESKD patients (not adjusted for covariates). Our finding of high risk of hospitalization in kidney transplant patients mirrors that of a case series of 36 consecutive kidney transplant patients at Montefiore where 28/36 (78%) were hospitalized’,

Again, multiple studies have since been been published regarding CoVID-19 in ESRD/ESKD patients and kidney transplant recipients.

Would consider discussing studies such as https://www.kidney-international.org/article/S0085-2538(20)30945-5/fulltext, for ESKD in addition to CMS reports.

Here are bigger studies on kidney transplant recipients for the discussion. The Montefiore study is small and is from early days of COVID-19- https://www.kidney-international.org/article/S0085-2538(20)30961-3/fulltext#.X0OiqBDfdDg.twitter

https://onlinelibrary.wiley.com/doi/full/10.1111/ajt.16185

Again, consider updating the manuscript with these more recent studies.

4. First paragraph- ‘The findings reported here identify pathophysiologies that impact the clinical course of COVID-19 and may be used to identify individuals at greatest risk for COVID-19-related complications’. The term ‘pathophysiologies’ seems misleading – consider using ‘co-morbidities’ instead.

5. Second paragraph- ‘How clinical conditions increase the risk of COVID-19-related complications is not known’. Again, as discussed before. Not necessarily an accurate statement as knowledge has quickly evolved. Consider updating or omitting.

6. Third paragraph- ‘Our findings also have implications for studies that seek to find genetic variants that alter the course of the disease (e.g. https://www.covid19hg.org/). We suspect that some genetic variants will be associated with these traits based on their effects on pre-existing clinical conditions’. We are not certain how this statement regarding genetic studies fits in the discussion. Consider clarifying or omitting.

7. We tend to agree with the authors that one of the most important limitations of their study is generalization as their population is mostly Caucasian. Also, consider adding race to table 1 of participants characteristics.

We hope these few comments will help the authors improve on their interesting manuscript and we thank the editor for entrusting our opinion.

Reviewer #2: This is an interesting study adding important information on the importance of chronic kidney disease as risk factor for COVID-19 associated hospitalization rate in the USA.

Comments:

Of high interest would be the outcome of the 354 patients admitted to the hospital. It would be very interesting to provide data on the outcome of those patients after hospitalisation, divided according to the different diseases/phenotypes (outcome: admission to ICU, need for ventilation, fatal outcome)? Did CKD patients (e.g. dialysis patients or stage IV and V CKD patients) have a higher risk for disease progression or death?

The high OR of 11 for hospitalisation with COVID-19 for patients with stage 5 CKD and/or ESKD is astonishing, especially when compared to other phenotypes, such as CHF. This point was not really adressed in the discussion. The renal tropism of SARS-CoV-2 and the high ACE2R expression in the kidneys are probably not part of the explanation. It would be interesting to have some discussion on this.

Additionally, what triggered the hospitalisation for those patients with COVID-19? Could it be that patients with ESKD were more likely to be hospitalized for fear of progression to a more serious disease and perhaps because of a strong interest in isolating these patients from other dialysis patients and keeping them away from dialysis units rather than because of the severity of COVID-19 disease? Since hospitalization was the endpoint of this study, there should be some comments on the reasons/triggers for hospitalization.

Table 1:

As reported in the text, there were 1,604 persons tested positive for SARS-CoV-2. But in table 1 only 1,250 positive patients are reported. Please correct.

Please explain the meaning of the numbers in the first line (inclusion Criteria, N (%)). Percent of what? Of 18,372 persons with testing for SARS-CoV-2 positivity, 12,971 fullfilled inclusion criteria of this study. This is 70.6% of all tested persons. The number of persons with fullfilling inclusion criteria and a negative SARS-CoV-2 Test were 11,367. But what does 72,4% refer to? It should be either 61,9% of the tested population or 87,6% of the tested population with inclusion criteria. Please, re-check your calculations and numbers of this table, especially line 1 and please explain.

Table 2:

Almost 50% of all CKD cases are „unspecified“ regarding to CKD stage (line 1). Since CKD stage seems to have tremendous impact on the COVID-19 ass. hospitalization risk, it would be most welcome if those 172 „unspecified stage “ patients could be re-classified according to eGFR data and added to each staged CKD phenotypes? In table 3 it is said, that eGFR data are available for 1393 individuals…

The small number of patients with stage IV and V/VD CKD lowers the power of this analysis. It does not seem biologically plausible that the Odds for hospitalisation with COVID-19 increases from 2,68 with CKD III to 11,85 with CKD IV.

In table 3 the numbers are much more plausible with the association of COVID-19 related hospitalization with eGFR stages. Stage III had an OR of (adj) 1,03, stage IV 2,90 and stage V 8,83. In table 3 unadjusted and adjusted OR were given. Please confirm that ORs in table 2 are adjusted values (should be also stated in the table legend)

Figure 1:

please explain why there are three (!) circles for chronic kindey diseases at different risk points in the graph (and two circels for acute renal faliure as well). This is confusing and should be clarified.

Further comments:

In the discussion the two terms "ESRD" and "ESKD" are used. This should be harmonized.

(literature suggestion on nomenclature in kidney diseases: NDT, Vol 35, 2020, 1077–1084, https://doi.org/10.1093/ndt/gfaa153

I suggest not to use the term „COVID-19 positive patients“, neither in the txt nor in table one. It should be either „SARS-CoV-2 positive“ or „SARS-CoV-2 infected patients“ or „patients with COVID-19“. ´COVID-19 is not a virus (SARS-CoV-2 is…), it’s a disease.

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Reviewer #1: No

Reviewer #2: No

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12 Oct 2020

We would like to thank the two reviewers for the insightful, and positive review of our manuscript. We have addressed all the points raised and edited the manuscript to reflect the changes as suggested by reviewers. Point by point response to each of the questions is denoted in red in the attached file..

Sincerely,

Tooraj Mirshahi

Submitted filename: Response_to_REVIEW_COMMENTS.docx

Click here for additional data file.

27 Oct 2020

PONE-D-20-26257R1

Electronic health record analysis identifies kidney disease as the leading risk factor for hospitalization in confirmed COVID-19 patients.

PLOS ONE

Dear Dr. Mirshahi,

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Harald Mischak

Academic Editor

PLOS ONE

Additional Editor Comments (if provided):

Theer are a few minor issues to be corrected, please change the paper accordingly and resubmit, so it can be accepted.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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Reviewer #2: Yes

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Reviewer #1: N/A

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Thank you for taking into account the comments of the reviewers in updating your manuscript. I have no further comments at this time.

Reviewer #2: I thank the authors for their efforts to adress our points raised..

Even though some of the points discussed in response to our comments could be part of an interesting debate I feel the paper is now improved and within the discussed limitations this paper really adds important information and insights to the current knowledge of risk factors for COVID-19 hospitalisations. I recommend it for publishing in PLOS ONE after very few very minor revisions:

a) Citation number 10 is cited twice in the first sentence of the discussion. Please correct.

b) Second sentence of the discussion: citation has sliped behind the sentence point

c) Citation number 10 is now incorrect. The current citation refers to an non-peer-reviewed draft of the article on a pre-print-server. The peer-reviewed final paper is published here: Brain Behav Immun. 2020 Jul; 87: 184–187. Published online 2020 May 23. doi: 10.1016/j.bbi.2020.05.059 PMCID: PMC7245300 PMID: 32454138

It has a new title as well: Lifestyle risk factors, inflammatory mechanisms, and COVID-19 hospitalization: A community-based cohort study of 387,109 adults in UK

d) there are a few more minor citation issues (e.g. citation 11 has blank spaces inbetween USA (U S A) or citation 14 - 16 should be combined into the same brackets), which will probably be adressed by the ediorial office before final proof.

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Reviewer #1: No

Reviewer #2: Yes: Ralph Wendt

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27 Oct 2020

Reviewers #2, Thanks for your keen eye for the details. As requested we updated ref#10, to the published version of that paper (the preveious citation was for the same paper in medRxiv).

We have reworked the citation punctuation for references #10-11 as well as 14-16, all in the discussion.

Submitted filename: Response_to_REVIEW_COMMENTS.docx

Click here for additional data file.

29 Oct 2020

Electronic health record analysis identifies kidney disease as the leading risk factor for hospitalization in confirmed COVID-19 patients.

PONE-D-20-26257R2

Dear Dr. Mirshahi,

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PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

4 Nov 2020

PONE-D-20-26257R2

Electronic health record analysis identifies kidney disease as the leading risk factor for hospitalization in confirmed COVID-19 patients

Dear Dr. Mirshahi:

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