Risk of chronic kidney disease in patients with heat injury: A nationwide longitudinal cohort study in Taiwan.

Global climate change has led to a significant increase in temperature over the last century and has been associated with significant increases in the severity and frequency of heat injury (HI). The consequences of HI included dehydration and rhabdomyolysis, leading to acute kidney injury, which is now recognized as a clear risk factor for chronic kidney disease (CKD). We aimed to investigate the effects of HI on the risk of CKD. This nationwide longitudinal population-based retrospective cohort study utilized the Taiwan National Health Insurance Research Database (NHIRD) data. We enrolled patients with HI who were followed in NHIRD system between 2000 and 2013.We excluded patients diagnosed with CKD or genital-urinary system-related disease before the date of the new HI diagnosis. The control cohort consisted of individuals without HI history. The patients and control cohort were selected by 1:4 matching according to the following baseline variables: sex, age, index year, and comorbidities. The outcome measure was CKD diagnosis. In total, 815 patients diagnosed with HI were identified. During the 13 year observation period, we identified 72 CKD events (8.83%) in the heat stroke group and 143 (4.38%) CKD events in the control group. Patients with heat stroke had an increased risk of CKD than the control patients (adjusted HR = 4.346, P < 0.001) during the follow-up period. The risk of end-stage renal disease was also significantly increased in the heat stroke group than in the control group (adjusted hazards ratio: 9.078, p < 0.001). HI-related CKD may represent one of the first epidemics due to global warming. When compared to those without HI, patients with HI have an increased CKD risk.

Introduction

Heat injury (HI) is the accumulation of heat resulting in the body's inability to tolerate it. Heat-related illness can range from mild conditions such as a vertigo, skin rash, or cramps to very serious conditions, such as heat syncope and heat exhaustion [1]. Heat stroke is the most severe heat-related illness and the characteristic of heatstroke is body temperature >40°C combined with neurologic dysfunction. Heat stroke is a type of severe heat illness with life-threatening injury requiring emergent and intensive care, and it accounts for 600 deaths a year in the United States [2]. In addition, the 28-day mortality rate of heat stroke has been reported to reach up to 58% [3]. Global climate change has led to a significant increase in temperature over the last century and has been associated with significant increases in the severity and frequency of HI.

Acute kidney injury (AKI) caused by HI is often combined with rhabdomyolysis. Rhabdomyolysis is a clinical and biochemical syndrome that occurs when the skeletal muscle cells disrupt and release creatine phosphokinase, lactate dehydrogenase, and myoglobin into the interstitial space and plasma. AKI occurs in 33%-50% of patients with rhabdomyolysis. Its etiology is multifactorial, which includes renal intraluminal cast formation, vasoconstriction, and direct myoglobin toxicity [4]. However, sustained AKI can lead to renal interstitial fibrosis, reductions in nephron number, insufficient blood supply, cell cycle disruption, and disrupted repair mechanisms, which eventually cause chronic kidney disease (CKD). In turn, CKD is also a risk factor of AKI development. Both diseases have a close relationship, are associated with an increased risk of nephron death, and can cause serious sequelae such as end-stage renal disease (ESRD) [5] However, it is unclear whether heat injury causes long-term renal damage.

Herein, we aimed to evaluate the relationship between HI and CKD using the National Health Insurance Research Database (NHIRD) of Taiwan. In addition to HI, other systemic co-morbidities were also examined in the multivariate analysis model to investigate whether HI is an independent risk factor for CKD.

Materials and methods

Data source

This retrospective population-based cohort study was approved by the Institutional Review Board (IRB) of Tri-Service General Hospital (IRB Registration Number: 2-105-05-082). All data are fully anonymized before we collected them and the IRB had waived the requirement for informed consent.

This study confirmed that all experiments were performed by relevant guidelines and regulations. The study enrolled all patients diagnosed with HI (ICD-9-CM 992.X) in Taiwan. Data from the NHIRD in Taiwan were used in this study [6]. The association between heat stroke and CKD events was investigated between 2000 to 2013 period. More than 99% of the population in Taiwan was covered by the National Health Insurance Program. The international Classification of Diseases, Ninth Revision (ICD-9) code was used for diagnosis [7]. The database in NHIRD also includes medications and patients’ demographics (such as socioeconomic status and residential area).

Patient selection

Patients diagnosed with HI from 2000 to 2013 were enrolled in the study group. To further enhance the diagnostic accuracy in the study group, only patients with the abovementioned ICD-9 code obtained from the emergency room (department code: 02) were included to ensure that only the patients with HI diagnosis were included.

In addition, patients were excluded if any of the criteria are present to remove any confounding factors: (1) received any renal surgery in the study period, (2) diagnose with CKD (ICD-9 code: 585) before the index date, and (3) presence of acute renal failure (ICD-9 code: 584.5), any genitourinary tract functional and infectious disease (ICD-9 codes: 593.81, 594.1, 596.0, 596.4, 596.5, 344.61, 596.5, 596.8, 597.80, 598.9, and 599.0), anomalies of the genital organ (ICD-9 codes: 752.9, 753.10,753.12), obstructive nephropathy (ICD-9 codes: 599.6, 591, 592.0, 592.1, 592.9, 593.3, 593.7), glomerulonephritis (ICD-9 codes: 580.9, 581.0, 582.1, 582.2, 582.4, 582.9, 583.4 583.4, 583.89, 583.9), and nephrotic syndrome (ICD-9 code: 581.X); and (5) age <18 years or >100 years.

The individuals in the study group were age-, sex-, and comorbidity-matched to a non-HI individual at a ratio of 1:4, who served as the control group. HI patients who could not be matched to non-HI individuals were excluded.

Main outcome measurement

The primary outcome in the current study was the development of CKD, which was represented with the ICD-9 code of 585 after the index date (Jan 1, 2000).

Individuals were identified as having CKD if they had a diagnosis should be confirmed for at least 3 consecutive times at intervals of at least 3 months according to ICD-9-CM code 585 (CKD); thus, the possibility to misdiagnose CKD is minimal. These CKD patients neither had other kidney-related conditions nor had received renal dialysis or a transplant before the cohort entry date. Thus, all patients had a primary diagnosis of CKD.

Patients with CKD were divided into subgroups according to their eGFR, which was calculated according to MDRD (modification of diet in renal disease study) equation [8]. Moreover, we also analyzed the CKD subgroup requiring renal replacement therapy. The renal replacement therapy subgroup enrolled CKD patients requiring hemodialysis or peritoneal dialysis for >3 months.

To increase the accuracy, we considered the effect of demographic conditions (i.e., age, sex, urbanization, and level of care) and the following comorbidity to standardize the baseline status in the study population: hypertension (ICD-9 codes: 401–405), hyperlipidemia (ICD-9 codes: 272.0–272.9), diabetes mellitus (DM) (ICD-9 codes: 250.x), cerebrovascular disease (ICD-9 codes: 362.34, 430.x–438.x), and congestive heart failure (ICD-9 codes: 398.91, 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, 425.4–425.9, 428.x).

We longitudinally traced the data from the index date until the date of CKD diagnosis between January 1, 2000 and December 31, 2013, withdrawal from the national health insurance program, death, or on 31 December 2013.

Statistical analysis

The baseline demographic condition and comorbidities were compared between the study and control groups using descriptive statistics. Chi-square test were used for categorical variables and t-test for continuous variables [9].

The two groups were adjusted carefully with respect to known confounders to ensure comparability during analyses. Multivariate Cox models were used simultaneously for age, sex, hypertension, hyperlipidemia, diabetes mellitus, heat failure, season, location, urbanization level, and level of care.

The Kaplan-Meier analysis was applied for the cumulative incidence curves of CKD for the two cohorts, and differences between cohorts were evaluated using the log-rank test. The incidence of CKD was estimated based on the Poisson distribution, with 95% confidence intervals (CIs). The odds ratios of CKD incidence rate after HI in the univariate analysis was used for the conditional logistic regression analyses.

In the outcomes analysis, P < 0.05 were interpreted as statistically significant. The type I error due to multiple testing was corrected by the Bonferroni method. The P value < 0.001 was considered to be significant for multiple comparisons. All data analyses were conducted using SPSS software version 22 (SPSS Inc., Chicago, IL, USA).

Results

In this study, a total of 1146 individuals diagnosed with heat stroke for the first time were enrolled. Of these, 331 patients with heat stroke were excluded (including heat stroke before index date, CKD before tracking, genitourinary system-related disease, age <18 years, unknown sex). After applying the exclusion criteria and four propensity score matching by sex, age, index date, and comorbidities. a total of 815 patients and 3260 matched controls were enrolled, as shown in Fig 1. During the 13-year follow-up, we identified 72 CKD events (8.83%) in the heat stroke group and 143 (4.38%) CKD events in the control group.

Fig 1

Patient selection flow chart.

CKD: Chronic kidney disease.

In Table 1, the baseline characteristics of the patients and controls were comparable in terms of sex, age, and comorbidity (hypertension, hyperlipidemia, diabetes mellitus, and stroke). Compared to patients without heat stroke, patients with heat stroke were more likely to live in middle or eastern Taiwan and outlet islands and in an area with lower urbanization level.

Table 1

Characteristics of study in the baseline.

Heat injury	Total		With		Without		P
Variables	n	%	n	%	n	%
Total	4,075	100	815	20	3,260	80
Gender							0.999
Male	3,345	82.09	669	82.09	2,676	82.09
Female	730	17.91	146	17.91	584	17.91
Age (years)	43.12 ± 18.40		42.74 ± 21.16		43.21 ± 17.64		0.514
HTN							0.159
Without	3,683	72.56	726	89.08	2,957	90.71
With	392	27.44	89	10.92	303	9.29
Hyperlipidemia							0.517
Without	3,980	78.06	799	98.04	3,181	97.58
With	95	21.94	16	1.96	79	2.42
DM							0.274
Without	3,531	69.08	716	87.85	2,815	86.35
With	544	30.92	99	12.15	445	13.65
Stroke							0.762
Without	3,992	76.37	780	95.71	3,112	95.46
With	183	23.63	35	4.29	148	4.54
HF							0.999
Without	4.225	79.02	805	98.77	3,220	98.77
With	50	20.98	10	1.23	40	1.23
Season							0.714
Spring	531	13.03	106	13.01	425	13.04
Summer	2,876	70.58	580	71.17	2,296	70.43
Autumn	491	12.05	90	11.04	401	12.30
Winter	177	4.34	39	4.78	138	4.23
Location in Taiwan							<0.001*
Northern	1,292	31.71	103	12.64	1,189	36.47
Middle	1,216	29.84	318	39.02	898	27.55
Southern	1,073	26.33	156	19.14	917	28.13
Eastern	432	10.60	207	25.40	225	6.90
Outlets islands	62	1.52	31	3.80	31	0.95
Urbanization level							<0.001*
1	618	15.17	39	4.79	579	17.76
2	1,660	40.74	361	44.29	1,299	39.85
3	584	14.33	98	12.02	486	14.91
4	1,213	29.77	317	38.90	896	27.48
Level of care							0.014
Medical center	1,085	26.63	246	30.18	839	25.74
Regional hospital	1,519	37.28	304	37.30	1,215	37.27
Local hospital	1,471	36.10	265	32.52	1,206	36.99

P-value (category variable: Chi-square/Fisher exact test; continuous variable: t-test)

HT = Hypertension; DM = Diabetes mellitus; HF = Heart failure.

*: The p value was obtained after Bonferroni correction.

In Fig 2, a Kaplan–Meier curve for cumulative CKD risk stratified by heat stroke with a log-rank test was shown. Patients with heat stroke were associated with a significantly increased risk of CKD events (log-rank P < 0.001). The incidence of CKD events was higher in the heat stroke group than in the control group from the first year of follow-up to the 13th year.

Fig 2

Kaplan-Meier curve for cumulative risk of chronic kidney disease in patients with and without heat injury.

CKD: Chronic kidney disease.

The adjusted hazards ratio (HR) of CKD events in the subgroup of cases with heat stroke and the matched controls are depicted in Table 2. Patients with heat stroke had higher CKD events than the controls (adjusted HR = 4.346, P < 0.001) during the follow-up period after adjusting for age, sex, insurance premium, comorbidities, urbanization level, patient care quality, and residential area in Taiwan. The risks of CKD events were associated with comorbidities (hypertension, hyperlipidemia, diabetes, and heart failure). There was a close relationship found between heat stroke episodes and CKD events.

Table 2

Hazard ratio of chronic kidney disease in association with baseline characteristics among heat injury patients in Cox model with competing risks.

Variables	Adjusted HR	95% CI	95% CI	P
Heat injury
Without	Reference
With	4.346	3.206	5.892	<0.001*
Gender
Male	1.187	0.864	1.630	0.290
Female	Reference
Age (years)	1.024	1.015	1.033	<0.001*
HTN
Without	Reference
With	1.615	1.421	1.897	0.012*
Hyperlipidemia
Without	Reference
With	1.181	1.044	1.736	0.017*
DM
Without	Reference
With	2.335	1.762	3.095	<0.001*
Stroke
Without	Reference
With	1.043	0.620	1.754	0.875
HF
Without	Reference
With	2.859	1.905	4.289	<0.001*
Season
Spring	Reference
Summer	0.936	0.627	1.397	0.745
Autumn	0.922	0.632	1.346	0.675
Winter	1.109	0.754	1.629	0.599
Location
Northern Taiwan	Reference
Middle Taiwan	1.031	0.720	1.478	0.866
Southern Taiwan	1.173	0.825	1.667	0.376
Eastern Taiwan	1.776	1.165	2.707	0.008
Outlets islands	1.697	0.415	6.939	0.462
Urbanization level
1 (The highest)	1.131	0.693	1.365	0.465
2	1.094	0.506	1.427	0.974
3	1.058	0.487	1.513	0.597
4 (The lowest)	Reference
Level of care
Medical center	1.065	0.675	1.684	0.787
Regional hospital	1.008	0.724	1.388	0.992
Local hospital	Reference

HR = hazard ratio, CI = confidence interval, Adjusted HR: Adjusted variables listed in the table

HT = Hypertension; DM = Diabetes mellitus; HF = Heart failure

*denotes P < .05 and was considered statistically significant.

We also investigated the risk of ESRD in the heat stroke and matched groups. We found that the proportion of patients with ESRD was significantly increased in the heat stroke group than in the matched group (Table 3) (adjusted HR: 9.078, p <0.001).

Table 3

Risk of ESRD receiving HD in patients with CKD by using Cox regression.

Heat injury	With HI vs. without HI(Reference)
	Events	Ratio	Adjusted HR	95%CI	P
Without HD	11	0.551	1.111	0.820–1.518	0.272
With HD	61	4.477	9.078	6.684–12.295	<0.001*

PYs = Person-years; Adjusted HR = Adjusted Hazard ratio: Adjusted for the variables listed in Table 2.; CI = confidence interval; ESRD = End stage renal disease; CKD = chronic kidney disease; HD = Hemodialysis; with HD represents heat injury patients with CKD and HD; without HD represents heat injury patients with CKD but without HD; ratio represents the risk of ESRD in HI vs non-HI.

Furthermore, the effect of HI on the CKD subgroups were examined according to ICD 9 codes in Table 4. The effect of HI was statistically significant in those CKD stage 2–5 and CKD of unknown stage group. HI patients compared with control group showed increased CKD stage 2 risk (95% CI = 1.038–2.014, adjusted HR: 1.432, P = 0.029), CKD stage 3 risk (95% CI = 4.232–8.496, adjusted HR: 5.265, P < 0.001), CKD stage 4 risk (95% CI = 5.998–11.225, adjusted HR: 7.984, P < 0.001), and CKD stage 5 risk (95% CI = 9.137–20.986, adjusted HR: 11.106, P < 0.001), respectively.

Table 4

Risk of different stages of CKD by using Cox regression.

Heat injury	With HI vs without HI (reference)
CKD stage	Events	Ratio	Adjusted HR	95%CI	P
I	3	0.387	0.787	0.511–1.098	0.485
II	7	0.710	1.432	1.038–2.014	0.029
III	22	3.021	5.265	4.232–8.496	<0.001*
IV	19	4.044	7.984	5.998–11.225	<0.001*
V	18	6.386	11.106	9.137–20.986	<0.001*
Unknown	3	2.554	5.134	3.726–7.133	<0.001*

Abbreviations: Adjusted HR = Adjusted Hazard ratio: Adjusted for the variables listed in Table 2.; CI: Confidence interval; CKD: Chronic kidney disease; HD: Hemodialysis

In S1 and S2 Tables, the mean follow-up time of the heat stroke and control groups were 10.40 ± 13.70 and 10.97 ± 9.84 years, respectively. The average time between HI and onset of CKD was 4.23 ± 2.87 years. In contrast, the average time in the matched group was 4.82 ± 4.12 years.

Discussion

In our retrospective population-based study of Taiwanese adults, patients with HI had a four-fold increased risk of CKD. Comorbidities including hypertension, hyperlipidemia, diabetes, and heart failure were also associated with higher incidence of CKD. Heat exhaustion is the most common HI during our 13-year follow-up period. HI was also associated with shorter progression duration to CKD than non-HI.

As the body temperatures increases, the occurrence of HI-associated complications, including central nervous system dysfunction and additional organ damage including AKI, liver injury, and rhabdomyolysis, is gradually increasing [10]. Recently, a population-based study enrolling 628 patients in Taiwan suggested that ischemic heart disease was independently associated with heat stroke in a Cox multivariate regression analysis [11]. Another retrospective study enrolled patients aged >65 years from 114 cities in USA and revealed that the impact of high temperature and heat waves increased the hospitalizations for renal disease (including acute/chronic glomerulonephritis, nephrotic syndrome, acute/chronic renal failure) and respiratory diseases (including bronchiectasis and chronic airway obstruction) [12]. A case-crossover study enrolling 19.17 million patients from New York, USA also demonstrated that high temperatures increased hospitalizations for acute renal failure, urinary tract infections, renal calculi, lower urinary calculi, and other lower urinary tract disorders [13]. Another time-series analysis concluded that high temperatures were associated with increases in morbidity and the relative risks of total emergency room visits and non-external hospitalizations [14].

Although the exact mechanism of how heat injury can cause CKD is unknown, there are several possibilities. The ability of the thermoregulation relies on the cutaneous vasodilatation and sweat gland excretion to cool the surface of the skin, thereby reducing body temperature. Moreover, heat-induced peripheral vasodilation and dehydration may involve decreased intestinal and solid organ blood supply, which leads to ischemia. Several studies have focused on exercise and showed that heat stresses can damage the gut structure and change its permeability. A malfunctioned intestinal tight junction barrier allows increased permeation of bacteria and endotoxins into the blood circulation [15]. Additionally, decreased blood flow through the renal artery can cause permanent damage to the kidney tissues and can increase the risk of acute or chronic renal failure.

A recent study has shown that heat stress induced the increase in plasma lipopolysaccharide concentration, anti-inflammatory cytokines (IL-10 and IL-1ra) and inflammatory responsive cytokines (IL-6) [16]. The heat stress response to endotoxemia and systemic inflammation is similar to sepsis, which causes profound alteration of the macro- and microcirculation of the kidney and maldistribution of blood supply to the organs. These dramatic changes cause a significant decrease in renal functional capillary circulation and induce renal ischemia [17]. A review article identified that patients with AKI had higher risks of developing CKD, ESRD, and mortality than those without AKI. AKI was an independent risk factor for CKD and ESRD [18].

In this study, we also found that HI increased the risk of CKD, especially in individuals with comorbidities (hypertension, hyperlipidemia, diabetes, and heart failure) and those living in the eastern area of Taiwan. A cross-sectional survey including 23,869 participants shows that diabetes, hypertension, and dyslipidemia are risk factors of CKD [19].

Furthermore, compared to healthy people, individuals with comorbidities (hypertension, hyperlipidemia, diabetes, and heart failure) who experience heat stress generally have lower cardiac output and cutaneous blood flow [20]; thus their ability to supply renal blood perfusion and radiate heat from the skin are low. As a result, the core body temperature increases easily, which may lead to prolonged renal ischemia and increased risk of CKD and ESRD. This result is consistent with the result we found that HI is strongly associated with the increased severity of CKD.

The results of our study should be interpreted in light of its limitations and strengths. To overcome the confounding bias, we utilized a propensity score from the baseline population to match the diversity in characteristics between heat stroke and control groups. Residual confounding bias such as lifestyle factors, including body mass index, smoking, alcohol drinking, and medication compliance was poorly measured in the NHIRD database. We adjusted these factors by including related diseases such as hypertension, hyperlipidemia, diabetes mellitus, and stroke. Another limitation of our study was that we defined heat stroke using the ICD-9-CM codes. We cannot evaluate the cooling process, presence of air conditioning, and the dynamic change in the core body temperature during heat stroke. In the future, we might perform a longitudinal study to evaluate our study outcomes. The strengths of our retrospective study included the national database derived from one million sampled cases, the use of propensity score matching between the cases and controls, and application of case-controlled matched index date to ensure comparability during analyses and minimize the confounders’ bias.

Conclusions

We found that heat stroke was associated with an approximately four-fold increase in risk of CKD and nine-fold increase in the risk of ESRD requiring long-term renal replacement therapy. Our results also demonstrated that the time interval to CKD progression decreases in heat stroke patients. Clinicians should continue to be alert for the appearance of CKD in high-risk patients with heat stroke.

Years of follow-up.

(DOCX)

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Years to CKD.

(DOCX)

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6 Jan 2020

PONE-D-19-33292

Risk of chronic kidney disease in patients with heat injury: A nationwide longitudinal cohort study in Taiwan

PLOS ONE

Dear Dr Chu,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The paper from Tseng et al entitled "Risk of chronic kidney disease in patients with heat injury: A nationwide longitudinal cohort study in Taiwan" is an interesting paper on a  nationwide longitudinal cohort study in Taiwan. The cohort was  followed up during 13 years. The authors showed that patients with heat stroke had twice higher CKD events than in the control group and that the risk of end-stage renal disease was also significantly increased in the heat stroke group.

Both reviewers asked for Major revision.

Mainly, the authors did not define the different CKD stages of the patients (from 1 to 5 in the Patient selection part in Material and method. The stages should be defined with an explanation of the method used to classify patients. We think this can improve the results of the study.

The authors should also follow recommendations of Reviewer 2 for statistical analysis.

Some minor modifications should also be made in order to answer reviewers.

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Additional Editor Comments:

The paper from Tseng et al entitled "Risk of chronic kidney disease in patients with heat injury: A nationwide longitudinal cohort study in Taiwan" is an interesting paper on a nationwide longitudinal cohort study in Taiwan. The cohort was followed up during 13 years. The authors showed that patients with heat stroke had twice higher CKD events than in the control group and that the risk of end-stage renal disease was also significantly increased in the heat stroke group.

Both reviewers asked for Major revision.

Mainly, the authors did not define the different CKD stages of the patients (from 1 to 5 in the Patient selection part in Material and method. The stages should be defined with an explanation of the method used to classify patients. We think this can improve the results of the study.

The authors should also follow recommendations of Reviewer 2 for statistical analysis.

Some minor modifications should also be made in order to answer reviewers.

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https://doi.org/10.1016/j.ejim.2018.09.019

https://doi.org/10.3390/ijerph16162865

https://doi.org/10.1016/j.radonc.2017.04.025

http://www.sjkdt.org/text.asp?2008/19/5/721/42439

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Reviewer #1: Tseng et al submit an original research paper entitled "Risk of chronic kidney disease in patients with heat injury: A nationwide longitudinal cohort study in Taiwan" In this work, they study the association between exposure to heat injury and onset of chronic kidney disease. Their longitudinal population-based retrospective cohort study used the Taiwan National Health Research Database data.

Authors selected adult patients diagnosed with HI that were followed up between 2000 and 2013. The outcome measure was CKD diagnosis. In total, 815 patients were diagnosed with HI, of which 72 were diagnosed with CKD. In total, that was twice more CKD diagnosis (in percentage) in the heat stroke group compared to the control group. The risk of end-stage

renal disease was also significantly increased in the heat stroke group.

The study is interesting and confirms in the Taiwanese population what was proposed by other studies, namely that

HI-related CKD may represent one of the first epidemics due to global warming.

The main drawback of the paper is that CKD diagnosis is very poorly defined; more informations have to be given.

Was CKD diagnosed according to eGFR? If yes, which equation was used to calculate it (CKD-EPI or other)?

The paper would gain a lot of relevance if the associations were to be performed again according to the various CKD stages, (1-5) or at least by subdividing patients in two groups , one for CKD stage1 to 3a, the other from CKD stage 3b to 5.

In figure 2, there seems to be an acceleration of CKD cases in the last three years in the HI group, can you please comment (heatwave in Taiwan or other explanation?)

Minor

Abstract

please correct "newly diagnosed with HI and were followed"

Introduction

Heat is repeated three times in "Heat injury (HI) is the accumulation of heat resulting in the body's inability to tolerate heat."

rephrase

reductions in nephron number, insufficient blood supply, cell cycle disruption, and disrupted repair mechanisms, which eventually CAUSE chronic kidney disease (CKD).

Rephrase sentence "In addition to HI, other systemic co-morbidities were also examined in the multivariate analysis model to investigate whether HI is an independent risk factor for CKD, which develops in the majority of the population."

Separate "Materials and methodsData source"

Results

separate "andcomorbidity ,"

Several word corrections still appear in the text, for example "The risk of CKD is was" and "in the heat stroke and matched groups," please correct

Please correct "Patients with heat stoke had higher CKD events"

Discussion rephrase ( HAs and repetition if increase) "A recent study HAS shown that heat stress induced the increase in plasma

lipopolysaccharide concentration and significant increases"

"which are in concordance with the findings in previous studies". Please cite them here

Reviewer #2: Kindly put in the LINE numbers so it facilitates identification of the lines that the reviewer needs to comment on. Also, the authors should apply correction procedures for multiple comparisons. This is the major part of my comments as it might impact on the conclusions.

**********

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Submitted filename: PONE-D-19-33292_Comments due 28 Dec 2019.docx

Click here for additional data file.

3 Apr 2020

Responses to the editor’s comments:

Comment 1: Please ensure that your manuscript meets PLOS ONE’s style requirements, including those for file naming.

Reply 1: The manuscript and file naming had been adjusted according to PLOS ONE’s style requirements.

Comment 2: In the ethics statement in the manuscript and in the online submission form, please provide additional information about the patient records/samples used in your retrospective study. Specifically, please ensure that you have discussed whether all data/samples were fully anonymized before you accessed them and/or whether the IRB or ethics committee waived the requirement for informed consent.

Reply 2: It had been corrected in the ethics statement on manuscript page 6, line 6 - 8.

This retrospective population-based cohort study was approved by the Institutional Review Board (IRB) of Tri-Service General Hospital (IRB Registration Number: 2-105-05-082). All data are fully anonymized before we collected them and the IRB had waived the requirement for informed consent.

Comment 3: We noticed you have some minor occurrence(s) of overlapping text with the following previous publications(s), which needs to be addressed. In your revision ensure you cite all your sources (including your own works), and quote or rephrase any duplicated text outside the Methods section. Further consideration is dependent on these concerns being addressed.

Reply 3: It had been rephrased and cited on page 6, line 11 - 17 and reference 6.

Data from the NHIRD in Taiwan were used in this study [6]. The association between heat stroke and CKD events was investigated between 2000 to 2013 period. More than 99% of the population in Taiwan was covered by the National Health Insurance Program. The international Classification of Diseases, Ninth Revision (ICD-9) code was used for diagnosis [7]. The database in NHIRD also includes medications and patients’ demographics (such as socioeconomic status and residential area).

Comnet 4: Please include a separate caption for each figure in your manuscript.

Reply 4: Separate caption had been included in page 10, line 13; page 12, line 15 and 16.

Response to the reviewers

Reviewer 1

Comment 1-1: Was CKD diagnosed according to eGFR? If yes, which equation was used to calculate it (CKD-EPI or other)?

Reply 1-1: It had been corrected on page 8, line 6 – 8.

Patients with CKD were divided into subgroups according to their eGFR, which was calculated according to MDRD (modification of diet in renal disease study) equation [8].

Comment 1-2: The paper would gain a lot of relevance if the associations were to be performed again according to the various CKD stages (1-5) or at least by subdividing patients in two groups, one for CKD stage 1 to 3a, the other from CKD stage 3b to 5.

Reply 1-2: It had been corrected on page 16, line 1 – 8 and page 16, table 5.

Furthermore, the effect of HI on the CKD subgroups were examined according to ICD 9 codes in Table 5. The effect of HI was statistically significant in those CKD stage 2-5 and CKD of unknown stage group. HI patients compared with control group showed increased CKD stage 2 risk (95% CI = 1.038-2.014, adjusted HR: 1.432, P = 0.029), CKD stage 3 risk (95% CI = 4.232-8.496, adjusted HR: 5.265, P < 0.001), CKD stage 4 risk (95% CI = 5.998-11.225, adjusted HR: 7.984, P < 0.001), and CKD stage 5 risk (95% CI = 9.137-20.986, adjusted HR: 11.106, P < 0.001) , respectively.

Comment 1-3: In figure 2, there seems to be an acceleration of CKD cases in the last 3 years in the HI group, can you please comment (heat wave in Taiwan or other explanation?)

Reply 1-3: It had been explained on page 12, line 10 – 13.

There seems to be an acceleration of CKD cases in the last 3 years in the HI group. It might be because of the annual average temperature was higher and there was more days with daily highest temperature > 35 oC in the last 3 years according to the database of Taiwan Central Weather Bureau.

Minor comment:

Abstract

Comment 1-4: Please correct “newly diagnosed with HI were followed”.

Reply 1-4: It had been corrected on page 3, line 10 and 11.

We enrolled patients with HI who were followed in NHIRD system between 2000 and 2013.

Introduction

Comment 1-5: Heat is repeated three times in “Heat injury (HI) is the accumulation of heat resulting in the body’s inability to tolerate heat.”

Reply 1-5: It had been corrected on page 4, line 11.

Heat injury (HI) is the accumulation of heat resulting in the body’s inability to tolerate it.

Rephrase

Comment 1-6: reductions in nephrons number, insufficient blood supply, cell cycle disruption, and disrupted repair mechanisms, which eventually CAUSE chronic kidney disease (CKD).

Reply 1-6: It had been corrected on page 5, line 9.

However, sustained AKI can lead to renal interstitial fibrosis, reductions in nephron number, insufficient blood supply, cell cycle disruption, and disrupted repair mechanisms, which eventually cause chronic kidney disease (CKD).

Comment 1-7: Rephrase sentence “In addition to HI, other systemic co-mobidities were also examined in the multivariate analysis model to investigate whether HI is an independent risk factor for CKD, which develops in the majority of the population.”

Reply 1-7: It had been rephrased on page 5, line 18 and 19.

In addition to HI, other systemic co-mobidities were also examined in the multivariate analysis model to investigate whether HI is an independent risk factor for CKD. , which develops in the majority of the population.

Comment 1-8: Separate “Materials and methodsData source”

Reply 1-8: It had been separated on page 6, line 3.

Results

Comment 1-9: separate “andcomorbidity”

Reply 1-9: It had been separated on page 10, line 16 and 17.

and comorbidity

Comment 1-10: Several word corrections still appear in the text, for example “The risk of CKD is was” and “in the heat stroke and matched groups,” please correct.

Reply 1-10: It had been corrected on page 12, line 8 and page15, line 5.

The risk of CKD is was more pronounced and was higher after the fourth year of follow-up in the heat stroke group than in the control group.

We also investigated the risk of ESRD in the heat stroke and matched groups. ,

Comment 1-11: Please correct “Patients with heat stroke had higher CKD events”

Reply 1-11: It had been corrected on page 12, line 19.

Patients with heat stroke had higher CKD events than the controls.

Comment 1-12: Discussion rephrase (Has and repetition if increase) “A recent study HAS shown that heat stress induced the increase in plasma lipopolysaccharide concentration and significant increases”

Reply 1-12: It had been rephrased on page 18, line 19 and 20.

A recent study have has shown that heat stress induced the increase in plasma lipopolysaccharide concentration, and significant increases in anti-inflammatory cytokines (IL-10 and IL-1ra ) and inflammatory responsive cytokines (IL-6)

Comment 1-13: “which are in concordance with the findings in previous studies”. Please cite them here

Reply 1-13: This sentence had been deleted on page 19, line 7 and 8.

Our results demonstrate the higher risk of CKD after heat stroke, which are in concordance with the findings in previous studies.

Response to the reviewer 2:

Comment 2-1: Kindly put in the LINE numbers so it facilitates identification of the lines that the reviewer needs to comment on.

Reply 2-1: Line numbers had been put in the manuscript.

Comment 2-2: Also, the authors should apply correction procedures for multiple comparisons. This is the major part of my comments as it might impact on the conclusions.

Reply 2-2: It had been corrected on page 9, line 18 and 19.

The type I error due to multiple testing was corrected by the Bonferroni method. The P value < 0.001 was considered to be significant for multiple comparisons.

Comment 2-3: The P in P-value is not consistently expressed… Sometimes P, other times p or even p

Reply 2-3: It had been corrected in the manuscript page 9, line 15 and 17; page 15, line 9.

Comment 2-4: Materials and methodsData source

--space

Reply 2-4: It had been corrected on page 6, line3.

Comment 2-5: The authors made no mention of correcting for multiple comparisons.

Reply 2-5: It had been corrected on page 9, line 18 and 19.

The type I error due to multiple testing was corrected by the Bonferroni method. The P value < 0.001 was considered to be significant for multiple comparisons.

Comment 2-6: Indicate the limitations in Discussion with letters or numbers.

Reply 2-6: It had been corrected on page 20, line 11.

The strengths of our retrospective study included the national database derived from 1 one million sampled cases.

Materials and methods

Comment 2-7: The diagnostic codes were recorded according to the International Classification of Diseases, Ninth Revision (ICD-9), Reference

Reply 2-7: It had been corrected, on page 23, line 6 - 8.

Reference [7] International Classification of Diseases, Ninth Revision (ICD-9), Centers for Disease Control and Prevention, 1998, available from https://www.cdc.gov/nchs/icd/icd9.htm

Statistical analysis

Comment 2-8: The baseline demographic condition and comorbidities were compared between the study and control groups using the x2 test for categorical variables and the t-test for continuous variables [6].

The authors use descriptive statistics in the Results. I suggest they put in about descriptive statistics here.

Reply 2-8: It had been corrected, on page 9, line 4.

The baseline demographic condition and comorbidities were compared between the study and control groups using descriptive statistics. Chi-square test were used for categorical variables and t-test for continuous variables.

Comment 2-9: In supplement Table 1, the median follow-up time of the heat stroke and control groups were 10.40 ± 13.70 and 10.97 ± 9.84 years, respectively

Median values look more like mean ± standard deviations. Median is usually followed range

Reply 2-9: It had been corrected, on page 16, line 15.

In Table S1-1 and S1-2, the median mean follow-up time of the heat stroke and control groups were 10.40 ± 13.70 and 10.97 ± 9.84 years, respectively.

Comment 2-10: A recent study have shown that heat stress induced the increase in plasma…

--- has

Reply 2-10: It had been rephrased on page 18, line 19.

A recent study have has shown that heat stress induced the increase in plasma lipopolysaccharide concentration,

Comment 2-11: Table 1. Characteristics of study in the baseline

Specify the Location

Reply 2-11: It had been corrected on page 11, table 1.

Location in Taiwan

Comment 2-12: Table 1-3. Use a corrective measure for the multiple P-values so that the authors minimize the risk of Type 1 error.

Reply 2-12: It had been corrected on page 9, line 18 and 19.

The type I error due to multiple testing was corrected by the Bonferroni method. The P value < 0.001 was considered to be significant for multiple comparisons.

Submitted filename: Response to reviewers.docx

Click here for additional data file.

30 Apr 2020

PONE-D-19-33292R1

Risk of chronic kidney disease in patients with heat injury: A nationwide longitudinal cohort study in Taiwan

PLOS ONE

Dear Dr CHU,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The statistic issues are not resolved.

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PLOS ONE

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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 #3: (No Response)

**********

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Reviewer #1: Yes

Reviewer #3: No

**********

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Reviewer #1: Yes

Reviewer #3: No

**********

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Reviewer #1: Yes

Reviewer #3: Yes

**********

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Reviewer #1: Yes

Reviewer #3: No

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: changes are ok for me. blablablablablablablablablablablablablablablablablablablablablablablablablabla

Reviewer #3: The authors present results from a study of heat injury (HI) and development of chronic kidney disease (CKD), using data from the Taiwan National Health Insurance Research Database. They found a higher risk of CKD in those with heat injury compared to matched controls. Results were further explored by type of heat injury as well as by severity of CKD. The manuscript will be strengthened if the authors consider the following points.

1. Authors need to add some additional information about the Taiwan National Health Insurance Research Database. What years does it cover? What specifically does it capture? For example, it is not clear if data prior to 2000 are available for review, but authors selected 2000-2013 as the study period or if the database started in 2000. This is particularly important for understanding the control group who the authors describe as "never having HI"

2. Authors need to discuss the method used for matching (Figure 1 mentions propensity-score matched controls, but this approach is not mentioned anywhere in the methods). Related to the propensity score, authors are encouraged to include the model results used as the basis for propensity score matching as a supplemental table.

3. Authors mention 95% confidence intervals for incidence, but these are not presented anywhere (line 10, page 9)

4. Authors state in the methods that they used the Bonferroni method for handling multiple comparisons, but never mention it again. Authors should make note of findings that survive the correction (I'm guessing this might be the * in the tables, but that is not specified).

5. Table 1 has percentages for categories calculated out of the entire sample, even for the With and Without groups. Such a percentage makes sense for the Total column, but for the With and Without columns, such a percentage is difficult to interpret especially with the 1:4 matching. The percentages should be calculated out of the "n" for that column, so that when looking at the p-value, the reader can directly look at the column percentages to understand the differences (or lack thereof).

6. Table 1 - the p-values associated with the comorbidities are not correct based on the frequencies provided. When I run chi-square or Fisher's exact tests on the contingency tables, I get highly significant differences between the With and Without groups on the comorbidity variables.

7. Figure 2: authors report the numbers under the figure as No. at risk, but these correspond to the numbers with CKD. The number at risk is more informative. It isn't clear why there are counts of CKD given at 0 years, since all individuals are free of CKD at 0. Also, for the HI group, it is not clear why the numbers under the figure stay at 72 for years 11, 12, 13, while there are jumps in the figure, which should correspond to new cases.

8. Table 3: Is model 2 a single model with the various categories of HI used as predictors? The use of "stratified by variables" in the table title makes it unclear exactly what was done. Some of the categories have very low n (and a low number of events) which makes them uninformative. Also, 95% CIs should be included for the Adjusted HRs.

Minor points:

1. line 15 in Abstract: The authors use "13-year follow-up", but they do not have 13 years of follow-up for all individuals. Authors might consider using "13 year observation period" or something similar.

2. line 17 in Abstract: "higher CKD events" should be "an increased risk of CKD"

3. line 14 on page 4: "Heat stroke is type of" should be "Heat stroke is a type of"

4. line 15 on page 4: "accounts to 600" should be "accounts for 600"

5. lines 11-12 on page 7: authors mention that HI patients that could not be matched were excluded - these aren't reported in Figure 1 anywhere - how many were excluded due to lack of a match?

6. line 17 on page 7: though readers can likely figure it out, authors should define "index date"

7. line 18 on page 7: "All enrolled patients had been diagnosed with CKD" is confusing, since not everyone had CKD. I'm guessing the authors mean to say something similar to "Individuals were identified as having CKD if they had a diagnosis"

8. line 16 on page 8: "or after 31 December 2013" is confusing - do the authors just mean to say "31 December 2013" since that is the end of the observation period?

9. lines 5-6 on page 9: the sentence starting with "Multivariate Cox models" is confusing, since models were not generated simultaneously for the list of variables - those variables were included in all of the models.

10. lines 10-12 on page 9: the sentence starting with "The odds ratios of CKD" is awkwardly phrased and should be reworded.

11. Figure 1: "nuknown" is an incorrect spelling (box for Exclusion criteria)

12. line 12 on page 10: Authors mention no difference in insurance premiums, but this information is not presented in Table 1

13. Note under Table 1: "continue" should be "continuous"

14. lines 2-9 on page 12: the description of the curves may not be needed, as there are no statistical tests performed to support the various statements. The larger jumps in the later years could be due to smaller numbers of people still at risk.

15. Tables 4-5 do not need to include the results for the overall sample, since that is already provided in Table 2. They also have the exact same title, so authors should come up with more descriptive table titles.

16. Tables 4 and 5 also need some clarification - for example, is the row for "With HD" corresponding to a model where the outcome is CKD with HD vs no CKD? What does "Ratio" represent? Also, there are two columns labeled 95% CI - I'm assuming these are the lower limit and the upper limit for the CI, but that should be clarified. The sample sizes in the groups (for example "With HD" or "CKD Stage II") should be given in the tables.

17. Tables S-1 and S-2: "medium" should be "median". Also, how are the max follow-up times greater than 13 years if the study period is from Jan 1, 2000 to Dec 31, 2013?

18. line 11 on page 16: "duration of onset of HI and CKD" should be "average time between HI and onset of CKD" or something similar and "duration" on the next line should be changed similarly.

19. last sentence of the results: did the authors do a statistical test to support this statement?

20. line 3 on page 17: "was associated with" should be "had a"

21. line 8 on page 17: "temperatures" should be "temperature"

22. line 18 on page 17: is the New York study really based on 19.17 million patients?

23: line 14 on page 19: "strongly association" should be "strongly associated"

**********

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Reviewer #1: Yes: Laurent METZINGER

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

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31 May 2020

Response to the reviewer 3:

Comment 1. Authors need to add some additional information about the Taiwan National Health Insurance Research Database. What years does it cover? What specifically does it capture? For example, it is not clear if data prior to 2000 are available for review, but authors selected 2000-2013 as the study period or if the database started in 2000. This is particularly important for understanding the control group who the authors describe as "never having HI"

Response 1 : Thanks for the reviewer’s comment. Taiwan launched a single-payer National Health Insurance program on March 1, 1995. The database of this program contains registration files and original claim data for reimbursement. Large computerized databases derived from this system by the National Health Insurance Administration. We use data from 1995 to 1999 as wash-out period to make sure the cases enrolled from 2000 are new cases.

Comment 2. Authors need to discuss the method used for matching (Figure 1 mentions propensity-score matched controls, but this approach is not mentioned anywhere in the methods). Related to the propensity score, authors are encouraged to include the model results used as the basis for propensity score matching as a supplemental table.

Response 2: Patients and controls were enrolled and propensity-score-matched (1:4) by age, sex, index date, comorbidities, and baseline medications.

Comment 3. Authors mention 95% confidence intervals for incidence, but these are not presented anywhere (line 10, page 9)

Response 3: This sentence had been deleted.

Comment 4. Authors state in the methods that they used the Bonferroni method for handling multiple comparisons, but never mention it again. Authors should make note of findings that survive the correction (I'm guessing this might be the * in the tables, but that is not specified).

Response 4: It had been mentioned in the note of table 1.

Comment 5. Table 1 has percentages for categories calculated out of the entire sample, even for the With and Without groups. Such a percentage makes sense for the Total column, but for the With and Without columns, such a percentage is difficult to interpret especially with the 1:4 matching. The percentages should be calculated out of the "n" for that column, so that when looking at the p-value, the reader can directly look at the column percentages to understand the differences (or lack thereof).

Response 5: The percentage of table 1 had been re-calculated according to the suggestion of reviewer.

Comment 6. Table 1 - the p-values associated with the comorbidities are not correct based on the frequencies provided. When I run chi-square or Fisher's exact tests on the contingency tables, I get highly significant differences between the With and Without groups on the comorbidity variables.

Response 6: Some of the frequencies in table 1 was misplaced and miscalculated. It had been corrected, with red words and marked.

Comment 7. Figure 2: authors report the numbers under the figure as No. at risk, but these correspond to the numbers with CKD. The number at risk is more informative. It isn't clear why there are counts of CKD given at 0 years, since all individuals are free of CKD at 0. Also, for the HI group, it is not clear why the numbers under the figure stay at 72 for years 11, 12, 13, while there are jumps in the figure, which should correspond to new cases.

Response 7: It is because the X-axis was not matched correctly with Y-axis. Figure 2 had been redrawn.

Comment 8. Table 3: Is model 2 a single model with the various categories of HI used as predictors? The use of "stratified by variables" in the table title makes it unclear exactly what was done. Some of the categories have very low n (and a low number of events) which makes them uninformative. Also, 95% CIs should be included for the Adjusted HRs.

Response 8: Table 3 had been deleted from the manuscript.

Minor points:

1. line 15 in Abstract: The authors use "13-year follow-up", but they do not have 13 years of follow-up for all individuals. Authors might consider using "13 year observation period" or something similar.

Response: It had been corrected on page 3, line 15.

2. line 17 in Abstract: "higher CKD events" should be "an increased risk of CKD"

Response: It had been corrected.

3. line 14 on page 4: "Heat stroke is type of" should be "Heat stroke is a type of"

Response: It had been corrected.

4. line 15 on page 4: "accounts to 600" should be "accounts for 600"

Response: It had been corrected.

5. lines 11-12 on page 7: authors mention that HI patients that could not be matched were excluded - these aren't reported in Figure 1 anywhere - how many were excluded due to lack of a match?

Response: This had been mentioned in new figure 1.

6. line 17 on page 7: though readers can likely figure it out, authors should define "index date"

Response: It had been defined.

7. line 18 on page 7: "All enrolled patients had been diagnosed with CKD" is confusing, since not everyone had CKD. I'm guessing the authors mean to say something similar to "Individuals were identified as having CKD if they had a diagnosis"

Response: It had been rephrased.

Individuals were identified as having CKD if they had a diagnosis should be confirmed…….

8. line 16 on page 8: "or after 31 December 2013" is confusing - do the authors just mean to say "31 December 2013" since that is the end of the observation period?

Response: It had been corrected.

or on 31 December 2013.

9. lines 5-6 on page 9: the sentence starting with "Multivariate Cox models" is confusing, since models were not generated simultaneously for the list of variables - those variables were included in all of the models.

Response: It had been corrected.

10. lines 10-12 on page 9: the sentence starting with "The odds ratios of CKD" is awkwardly phrased and should be reworded.

Response: It had been rephrased.

The odds ratios of CKD incidence rate after HI with and without adjustments for covariates with P < 0.05 in the univariate analysis was used for the conditional logistic regression analyses.

11. Figure 1: "nuknown" is an incorrect spelling (box for Exclusion criteria)

Response: It had been corrected.

12. line 12 on page 10: Authors mention no difference in insurance premiums, but this information is not presented in Table 1

Response: It had been deleted.

insurance premiums (in New Taiwan dollar $),

13. Note under Table 1: "continue" should be "continuous"

Response: It had been corrected.

14. lines 2-9 on page 12: the description of the curves may not be needed, as there are no statistical tests performed to support the various statements. The larger jumps in the later years could be due to smaller numbers of people still at risk.

Response: The description of the curves had been deleted.

15. Tables 4-5 do not need to include the results for the overall sample, since that is already provided in Table 2. They also have the exact same title, so authors should come up with more descriptive table titles.

Response: The overall sample had been deleted. The table titles had been changed.

16. Tables 4 and 5 also need some clarification - for example, is the row for "With HD" corresponding to a model where the outcome is CKD with HD vs no CKD? What does "Ratio" represent? Also, there are two columns labeled 95% CI - I'm assuming these are the lower limit and the upper limit for the CI, but that should be clarified. The sample sizes in the groups (for example "With HD" or "CKD Stage II") should be given in the tables.

Response: It had been corrected in table 4 and 5 (changed to table 3 and 4).

17. Tables S-1 and S-2: "medium" should be "median". Also, how are the max follow-up times greater than 13 years if the study period is from Jan 1, 2000 to Dec 31, 2013?

Response: It had been corrected in tables S-1 and S-2. The duration is 14 years from Jan 1, 2000 to Dec 31, 2013.

(2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013; totally 14 years.)

18. line 11 on page 16: "duration of onset of HI and CKD" should be "average time between HI and onset of CKD" or something similar and "duration" on the next line should be changed similarly.

Response: It had been corrected.

19. last sentence of the results: did the authors do a statistical test to support this statement?

Response: The last sentence had been deleted.

20. line 3 on page 17: "was associated with" should be "had a"

Response: It had been corrected.

21. line 8 on page 17: "temperatures" should be "temperature"

Response: It had been corrected.

22. line 18 on page 17: is the New York study really based on 19.17 million patients?

Response: The study population in that study included all residents of New York State. The population of New York State was 19.17 million.

23: line 14 on page 19: "strongly association" should be "strongly associated"

Response: It had been corrected.

Submitted filename: response to the reviewer3.docx

Click here for additional data file.

19 Jun 2020

Risk of chronic kidney disease in patients with heat injury: A nationwide longitudinal cohort study in Taiwan

PONE-D-19-33292R2

Dear Dr. Chu,

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The paper is accepted.

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Reviewer #3: All comments have been addressed

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Reviewer #3: (No Response)

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Reviewer #3: No

24 Jun 2020

PONE-D-19-33292R2

Risk of chronic kidney disease in patients with heat injury: A nationwide longitudinal cohort study in Taiwan

Dear Dr. Chu:

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

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

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PLOS ONE