Early clinical factors predicting the development of critical disease in Japanese patients with COVID‐19: A single‐center, retrospective, observational study

  1. Takatoshi Higuchi
  2. Tsutomu Nishida
  3. Hiromi Iwahashi
  4. Osamu Morimura
  5. Yasushi Otani
  6. Yukiyoshi Okauchi
  7. Masaru Yokoe
  8. Norihiro Suzuki
  9. Masami Inada
  10. Kinya Abe

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Abstract

The factors predicting the progression of coronavirus disease‐2019 (COVID‐19) from mild to moderate to critical are unclear. We retrospectively evaluated risk factors for disease progression in Japanese patients with COVID‐19. Seventy‐four patients with laboratory‐confirmed COVID‐19 were hospitalized in our hospital between February 20, 2020, and June 10, 2020. We excluded asymptomatic, non‐Japanese, and pediatric patients. We divided patients into the stable group and the progression group (PG; requiring mechanical ventilation). We compared the clinical factors. We established the cutoff values (COVs) for significantly different factors via receiver operating characteristic curve analysis and identified risk factors by univariate regression. We enrolled 57 patients with COVID‐19 (median age 52 years, 56.1% male). The median time from symptom onset to admission was 8 days. Seven patients developed critical disease (PG: 12.2%), two (3.5%) of whom died; 50 had stable disease. Univariate logistic analysis identified an elevated lactate dehydrogenase (LDH) level (COV: 309 U/l), a decreased estimated glomerular filtration rate (eGFR; COV: 68 ml/min), lymphocytopenia (COV: 980/μl), and statin use as significantly associated with disease progression. However, in the Cox proportional hazards analysis, lymphocytopenia at admission was not significant. We identified three candidate risk factors for progression to critical COVID‐19 in adult Japanese patients: statin use, elevated LDH level, and decreased eGFR.

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  1. Version published to 10.1002/jmv.26599
  2. ScreenIT

    SciScore for 10.1101/2020.07.29.20159442: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: The present study was conducted in accordance with the Declaration of Helsinki, and approval was obtained from the Institutional Review Board of Toyonaka Municipal Hospital (No. 2020-07-01).
    Consent: The requirement for informed consent was waived via the opt-out method on our hospital website.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analyses were performed with JMP statistical software (ver. 14.3, SAS Institute, Inc.
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    This study has several limitations due to its retrospective nature. First, we enrolled only a small number of COVID-19 patients. Consequently, we did not perform multivariate analysis. Second, our dataset had missing data, because we were not very accustomed to seeing COVID-19 patients and avoided unnecessary or nonurgent contact with these patients to reduce the risk of infection. Therefore, we statistically handled missing data using imputation methods in statistical software. In conclusion, we first reported three candidate risk factors in Japanese adult patients with mild to moderate COVID-19: statin use, an elevated LDH level, and decreased eGFR.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. ScreenIT

    SciScore for 10.1101/2020.07.29.20159442: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementThe present study was conducted in accordance with the Declaration of Helsinki, and approval was obtained from the Institutional Review Board of Toyonaka Municipal 6 Hospital (No. Statistical analysis 7 The medians and interquartile ranges (IQRs) are reported for continuous variables.Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analyses were performed with JMP statistical software (ver. 14.3, SAS Institute, Inc., Cary, NC, USA).
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

    13 This study has several limitations due to its retrospective nature. First, we enrolled only a small number of COVID-19 patients. Consequently, we did not perform multivariate analysis. Second, our dataset had missing data, because we were not very accustomed to seeing COVID-19 patients and avoided unnecessary or nonurgent contact with these patients to reduce the risk of infection. Therefore, we statistically handled missing data using imputation methods in statistical software. In conclusion, we first reported three candidate risk factors in Japanese adult patients with mild to moderate COVID-19: statin use, an elevated LDH level, and decreased eGFR. 14 Figure legends Figure 1. Flow chart of patient enrollment. Figure 2. Overall progression-free interval in patients with mild to moderate COVID-19. Significant differences were found in the progression-free interval between patients stratified by statin use, LDH levels and eGFR on admission. 15 Acknowledgments and collaborators We thank all medical staff and doctors at Toyonaka Municipal Hospital. The collaborators involved in the study are as follows: Sanae Fukuda, Kazumi Ohkubo (Nursing Department), Dr. Masashi Yamamoto, Dr. Kengo Matsumoto, Dr. Kaori Mukai, Dr. Dai Nakamatsu, Dr. Aya Sugimoto, Dr. Naoto Osugi, Dr. Sho Yamaoka, Dr. Tatsuya Sakamoto, Dr. Akino Okamoto, Dr. Yuri Tsujii, Dr. Ryo Sugio, Dr. Kazumasa Souma (Department of Gastroenterology), Dr. Masayuki Moriya, Dr. Katsuya Araki, Dr. Yuri Sugiura (Department of Neurology), Dr. Masanobu Takeji, Dr. Satoko Yamamoto, Dr. Yasuo Kusunoki, Dr. Natsuko Ikeda, Dr. Kumie Teramoto, Dr. Momoko Okawara, Dr. Yuki Iwahashi, Dr. Masashi Yokoyama, Dr. Toru Kida, Dr. Chihiro Hasegawa, Dr. Shunsuke Shiode, Dr. Tomoko Isaka, Dr. Naohiko Ito, Dr. Kanae Matsuno (Department of Internal Medicine), Dr. Yukinori Okazaki, Dr. Yukika Mizukami, Dr. Takuma Iida, Dr. Naoki Fukushima, Dr. Ai Miyaoka, Dr. Takamori Yamamoto (Department of Cardiology). 16 Table 1. Characteristics and clinical course of patients with mild to moderate confirmed COVID-19 Patients with mild to moderate COVIDCharacteristics N=57 Age, median (IQR) 52 (35, 69.5) Male Sex, n (%) Body mass index, median (IQR) 32 (56.1) 23.8 (21.0, 26.5) Pneumonia, n (%) 37 (64.9) Smoking history (Non/past/current) History of close contact with individuals with confirmed cases Days from onset of symptoms to admission, median (IQR) Comorbidities 33/17/7 Hypertension, n (%) Cardiovascular diseases, n (%) Chronic obstructive pulmonary disease, n (%) Asthma, n (%) 16 (28.1) 8 (5, 12) 16 (28.1) 5 (8.8) 4 (7.0) 8 (14.0) Diabetes mellitus, n (%) 13 (22.8) Hyperlipidemia, n (%) 20 (35.1) Chronic kidney disease, n (%) 5 (8.8) Hemodialysis, n (%) 3 (5.3) Solid cancer, n (%) 1 (1.8) Pregnancy, n (%) 2 (3.5) Use of medication for comorbidities ARB, n (%) 8 (14.0) Calcium blocker, n (%) 9 (15.8) Statin, n (%) 12 (21.1) 17 Table 2. Initial presentation, treatment, and clinical course Initial presentation Fever, n (%) 51 (89.9) Fatigue, n (%) 12 (21.1) Cough, n (%) 5 (8.8) Dyspnea, n (%) 13 (22.8) Sputum production, n (%) 5 (8.8) Anorexia, n (%) 7 (12.3) Headache, n (%) 5 (8.8) Diarrhea, n (%) 14 (25.9) New loss of taste or smell, n (%) 9 (15.8) Erythema, n (%) 3 (5.3) Severity of COVID-19 Mild to moderate/Severe, n (%) 37 (64.9)/20 (35.1) Treatment Required oxygen, n (%) 20 (35.1) Medication for COVID-19 Ciclesonide, n (%) 29 (50.9) Hydroxychloroquine, n (%) 14 (24.6) Favipiravir, n (%) 12 (21.1) Clinical course Length of hospital stay, median (IQR) (days) 12 (8, 20) Required mechanical ventilatory support, n (%) 7 (12.3) Mortality, n (%) 2 (3.5) 18 Table 3. Comparison of the progression group and stable group. Progression group Stable group haracteristics N=7 N=50 48 (31, 69.3) ge, median (IQR) 61 (59, 71) 27 (54.0) x, male (%) 5 (71.4) P-val 0.122 0.449 24.9 (21.1, 25.6) 24.6 (21.0, 22.9) 0.679 2/3/2 31/14/5 0.186 9 (7, 9) N.A. 1 (0, 3) N.A. 0/7 37/13 0.000 2 (28.6) 0 (0) 0.013 ny comorbidity, n (%) 6 (85.7) 29 (58) 0.230 ypertension, n (%) 3 (42.9) 13 (26.0) 0.387 0 (0) 5 (10.2) 1.00 0 (0) 4 (8.0) 1.00 0 (0) 8 (16.3) 0.576 Diabetes mellitus, n (%) 2 (28.6) 11 (22) 0.659 Hyperlipidemia, n (%) 4 (57.1) 16 (32.0) 0.226 Chronic kidney disease, n (%) 1 (14.3) 4 (8) 0.494 Hemodialysis, n (%) 0 (0) 3 (6.0) 1.00 Solid cancer, n (%) 0 (0) 1 (2.0) 1.00 Pregnancy, n (%) 0 (0) 2 (4.0) 1.00 ARB, n (%) 1 (14.3) 7 (14.0) 1.00 Calcium blocker, n (%) 3 (42.9) 6 (12) 0.070 Statin, n (%) 4 (57.1) 8 (16) 0.029 8 (5, 9) 8 (5, 13.3) 0.608 4600 (3800, 7000) 5950 (4675, 7500) 0.318 1231 (840, 1553) 0.003 ody mass index, median (IQR) moking (Non/past/current) ays from onset of symptoms to sease progression, median (IQR) ays from admission to disease ogression, median (IQR) verity of COVID-19 ild to moderate/sever ortality omorbidities Cardiovascular diseases, n (%) Chronic obstructive pulmonary sease, n (%) Asthma, n (%) edication itial assessment ays from the onset of symptoms to mission, median (IQR) BC, median (IQR) count/μl Lymphocyte, median (IQR) ount/μl) 617 (374, 864) 19 Neutrophilia, median (IQR) ount/μl) emoglobin, median (IQR) (g/dl) 4049 (3001, 5508) 0.845 14.2 (12.4, 14.5) 14.3 (12.0, 15.2) 0.855 18.7 (14.1, 23.2) 21.1 (16.4, 29.3) 0.185 DH, median (IQR) (IU/L) 450 (309, 562) 263 (205, 323) 0.007 RP, median (IQR) (mg/dl) 5.69 (3.73, 10.9) 2.11 (0.2, 6.51) 0.026 ST, median (IQR) (IU/L) 51 (31, 85) 39 (21, 55) 0.066 LT, median (IQR) (IU/L) 41 (29, 63) 35 (18, 58) 0.618 0.51 (0.4, 1.29) 0.61 (0.49, 0.72) 0.626 bA1c, median (IQR) (%) 8.2 (7.9, 9.5) 7.5 (6.8, 8.4) 0.084 , median (IQR) (mg/dl) 0.99 (0.84, 1.3) 0.8 (0.66, 1.1) 0.084 17 (14, 25) 13 (10, 17) 0.039 56.8 (46.8, 65.6) 76.8 (63.5, 91.7) 0.009 atelet count, median (IQR) (109/L) otal bilirubin, median (IQR) (mg/dl) UN, median (IQR) (mg/dl) GFR, median (IQR) 3795 (2690, 5859) ARB, angiotensin receptor blocker; LDH, lactate dehydrogenase; CRP, C-reactive protein; AST, aspartate aminotransferase; Cr, creatinine; BUN, blood urea nitrogen; eGFR, estimated glomerular filtration rate 20 Table 4. Univariate logistic analysis and univariate Cox proportional hazards analysis of risk factors for progression to critical COVID-19 Univariate Cox proportional Univariate logistic analysis hazards analysis Odds Hazard Characteristics 95% CI P-value 95% CI P-value ratio ratio Statin use 1 No 1 Yes Lymphocyte count >980 8.0 <980 9.8 1.46-43.7 0.0163 6.3 1.40-28.3 0.0167 0.93-64.1 0.0584 110 0.0169 N.C 0.9989 1.23-84.8 0.0315 1 1 1.09-87.7 0.0414 7.7 LDH <309 1 >309 14.0 1 1.55-123 0.0188 13.3 CRP <2.92 1 >2.92 N.C 1 N.C 0.9960 N.C eGFR >68 1 <68 12.8 1 1.41-115 0.0233 10.2 CI, confidence interval; LDH, lactate dehydrogenase; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; N.C, not calculated 21

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  4. ScreenIT

    SciScore for 10.1101/2020.07.29.20159442: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementThe present study was conducted in accordance with the Declaration of Helsinki, and approval was obtained from the Institutional Review Board of Toyonaka Municipal 6 Hospital (No. Statistical analysis 7 The medians and interquartile ranges (IQRs) are reported for continuous variables.Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analyses were performed with JMP statistical software (ver. 14.3, SAS Institute, Inc., Cary, NC, USA).
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

    13 This study has several limitations due to its retrospective nature. First, we enrolled only a small number of COVID-19 patients. Consequently, we did not perform multivariate analysis. Second, our dataset had missing data, because we were not very accustomed to seeing COVID-19 patients and avoided unnecessary or nonurgent contact with these patients to reduce the risk of infection. Therefore, we statistically handled missing data using imputation methods in statistical software. In conclusion, we first reported three candidate risk factors in Japanese adult patients with mild to moderate COVID-19: statin use, an elevated LDH level, and decreased eGFR. 14 Figure legends Figure 1. Flow chart of patient enrollment. Figure 2. Overall progression-free interval in patients with mild to moderate COVID-19. Significant differences were found in the progression-free interval between patients stratified by statin use, LDH levels and eGFR on admission. 15 Acknowledgments and collaborators We thank all medical staff and doctors at Toyonaka Municipal Hospital. The collaborators involved in the study are as follows: Sanae Fukuda, Kazumi Ohkubo (Nursing Department), Dr. Masashi Yamamoto, Dr. Kengo Matsumoto, Dr. Kaori Mukai, Dr. Dai Nakamatsu, Dr. Aya Sugimoto, Dr. Naoto Osugi, Dr. Sho Yamaoka, Dr. Tatsuya Sakamoto, Dr. Akino Okamoto, Dr. Yuri Tsujii, Dr. Ryo Sugio, Dr. Kazumasa Souma (Department of Gastroenterology), Dr. Masayuki Moriya, Dr. Katsuya Araki, Dr. Yuri Sugiura (Department of Neurology), Dr. Masanobu Takeji, Dr. Satoko Yamamoto, Dr. Yasuo Kusunoki, Dr. Natsuko Ikeda, Dr. Kumie Teramoto, Dr. Momoko Okawara, Dr. Yuki Iwahashi, Dr. Masashi Yokoyama, Dr. Toru Kida, Dr. Chihiro Hasegawa, Dr. Shunsuke Shiode, Dr. Tomoko Isaka, Dr. Naohiko Ito, Dr. Kanae Matsuno (Department of Internal Medicine), Dr. Yukinori Okazaki, Dr. Yukika Mizukami, Dr. Takuma Iida, Dr. Naoki Fukushima, Dr. Ai Miyaoka, Dr. Takamori Yamamoto (Department of Cardiology). 16 Table 1. Characteristics and clinical course of patients with mild to moderate confirmed COVID-19 Patients with mild to moderate COVIDCharacteristics N=57 Age, median (IQR) 52 (35, 69.5) Male Sex, n (%) Body mass index, median (IQR) 32 (56.1) 23.8 (21.0, 26.5) Pneumonia, n (%) 37 (64.9) Smoking history (Non/past/current) History of close contact with individuals with confirmed cases Days from onset of symptoms to admission, median (IQR) Comorbidities 33/17/7 Hypertension, n (%) Cardiovascular diseases, n (%) Chronic obstructive pulmonary disease, n (%) Asthma, n (%) 16 (28.1) 8 (5, 12) 16 (28.1) 5 (8.8) 4 (7.0) 8 (14.0) Diabetes mellitus, n (%) 13 (22.8) Hyperlipidemia, n (%) 20 (35.1) Chronic kidney disease, n (%) 5 (8.8) Hemodialysis, n (%) 3 (5.3) Solid cancer, n (%) 1 (1.8) Pregnancy, n (%) 2 (3.5) Use of medication for comorbidities ARB, n (%) 8 (14.0) Calcium blocker, n (%) 9 (15.8) Statin, n (%) 12 (21.1) 17 Table 2. Initial presentation, treatment, and clinical course Initial presentation Fever, n (%) 51 (89.9) Fatigue, n (%) 12 (21.1) Cough, n (%) 5 (8.8) Dyspnea, n (%) 13 (22.8) Sputum production, n (%) 5 (8.8) Anorexia, n (%) 7 (12.3) Headache, n (%) 5 (8.8) Diarrhea, n (%) 14 (25.9) New loss of taste or smell, n (%) 9 (15.8) Erythema, n (%) 3 (5.3) Severity of COVID-19 Mild to moderate/Severe, n (%) 37 (64.9)/20 (35.1) Treatment Required oxygen, n (%) 20 (35.1) Medication for COVID-19 Ciclesonide, n (%) 29 (50.9) Hydroxychloroquine, n (%) 14 (24.6) Favipiravir, n (%) 12 (21.1) Clinical course Length of hospital stay, median (IQR) (days) 12 (8, 20) Required mechanical ventilatory support, n (%) 7 (12.3) Mortality, n (%) 2 (3.5) 18 Table 3. Comparison of the progression group and stable group. Progression group Stable group haracteristics N=7 N=50 48 (31, 69.3) ge, median (IQR) 61 (59, 71) 27 (54.0) x, male (%) 5 (71.4) P-valu 0.122 0.449 24.9 (21.1, 25.6) 24.6 (21.0, 22.9) 0.679 2/3/2 31/14/5 0.186 9 (7, 9) N.A. 1 (0, 3) N.A. 0/7 37/13 0.000 2 (28.6) 0 (0) 0.013 ny comorbidity, n (%) 6 (85.7) 29 (58) 0.230 ypertension, n (%) 3 (42.9) 13 (26.0) 0.387 0 (0) 5 (10.2) 1.000 0 (0) 4 (8.0) 1.000 0 (0) 8 (16.3) 0.576 Diabetes mellitus, n (%) 2 (28.6) 11 (22) 0.659 Hyperlipidemia, n (%) 4 (57.1) 16 (32.0) 0.226 Chronic kidney disease, n (%) 1 (14.3) 4 (8) 0.494 Hemodialysis, n (%) 0 (0) 3 (6.0) 1.000 Solid cancer, n (%) 0 (0) 1 (2.0) 1.000 Pregnancy, n (%) 0 (0) 2 (4.0) 1.000 ARB, n (%) 1 (14.3) 7 (14.0) 1.000 Calcium blocker, n (%) 3 (42.9) 6 (12) 0.070 Statin, n (%) 4 (57.1) 8 (16) 0.029 8 (5, 9) 8 (5, 13.3) 0.608 4600 (3800, 7000) 5950 (4675, 7500) 0.318 1231 (840, 1553) 0.003 ody mass index, median (IQR) moking (Non/past/current) ays from onset of symptoms to sease progression, median (IQR) ays from admission to disease ogression, median (IQR) verity of COVID-19 ild to moderate/sever ortality omorbidities Cardiovascular diseases, n (%) Chronic obstructive pulmonary sease, n (%) Asthma, n (%) edication itial assessment ays from the onset of symptoms to mission, median (IQR) BC, median (IQR) count/μl Lymphocyte, median (IQR) ount/μl) 617 (374, 864) 19 Neutrophilia, median (IQR) ount/μl) emoglobin, median (IQR) (g/dl) 4049 (3001, 5508) 0.845 14.2 (12.4, 14.5) 14.3 (12.0, 15.2) 0.855 18.7 (14.1, 23.2) 21.1 (16.4, 29.3) 0.185 DH, median (IQR) (IU/L) 450 (309, 562) 263 (205, 323) 0.007 RP, median (IQR) (mg/dl) 5.69 (3.73, 10.9) 2.11 (0.2, 6.51) 0.026 ST, median (IQR) (IU/L) 51 (31, 85) 39 (21, 55) 0.066 LT, median (IQR) (IU/L) 41 (29, 63) 35 (18, 58) 0.618 0.51 (0.4, 1.29) 0.61 (0.49, 0.72) 0.626 bA1c, median (IQR) (%) 8.2 (7.9, 9.5) 7.5 (6.8, 8.4) 0.084 , median (IQR) (mg/dl) 0.99 (0.84, 1.3) 0.8 (0.66, 1.1) 0.084 17 (14, 25) 13 (10, 17) 0.039 56.8 (46.8, 65.6) 76.8 (63.5, 91.7) 0.009 atelet count, median (IQR) (109/L) otal bilirubin, median (IQR) (mg/dl) UN, median (IQR) (mg/dl) GFR, median (IQR) 3795 (2690, 5859) ARB, angiotensin receptor blocker; LDH, lactate dehydrogenase; CRP, C-reactive protein; AST, aspartate aminotransferase; Cr, creatinine; BUN, blood urea nitrogen; eGFR, estimated glomerular filtration rate 20 Table 4. Univariate logistic analysis and univariate Cox proportional hazards analysis of risk factors for progression to critical COVID-19 Univariate Cox proportional Univariate logistic analysis hazards analysis Odds Hazard Characteristics 95% CI P-value 95% CI P-value ratio ratio Statin use 1 No 1 Yes Lymphocyte count >980 8.0 <980 9.8 1.46-43.7 0.0163 6.3 1.40-28.3 0.0167 0.93-64.1 0.0584 110 0.0169 N.C 0.9989 1.23-84.8 0.0315 1 1 1.09-87.7 0.0414 7.7 LDH <309 1 >309 14.0 1 1.55-123 0.0188 13.3 CRP <2.92 1 >2.92 N.C 1 N.C 0.9960 N.C eGFR >68 1 <68 12.8 1 1.41-115 0.0233 10.2 CI, confidence interval; LDH, lactate dehydrogenase; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; N.C, not calculated 21

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  5. ScreenIT

    SciScore for 10.1101/2020.07.29.20159442: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementThe present study was conducted in accordance with the Declaration of Helsinki, and approval was obtained from the Institutional Review Board of Toyonaka Municipal Hospital (No. 2020-07-01).Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variableResults: We enrolled 57 COVID-19 patients (median age 52 years, 56.1% male).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analyses were performed with JMP statistical software (ver. 14.3, SAS Institute, Inc.
    SAS Institute
    suggested: (Statistical Analysis System, SCR_008567)

    Data from additional tools added to each annotation on a weekly basis.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  6. Version published to 10.1101/2020.07.29.20159442 on medRxiv