The psychological distress and sleep problems caused by current Covid-19 outbreak is not well known in kidney transplant recipients. In this study, we aimed to investigate the association between perceived stress with sleep quality, insomnia, anxiety, depression and kidney function in kidney transplant recipients during the Covid-19 pandemic.A hundred-six kidney transplant recipients were enrolled. Questionnaire of "Socio-demographics", "Perceived Stress Scale (PSS)", "Pittsburgh Sleep Quality Index (PSQI)", "Insomnia Severity Index (ISI)" and “Hospital Anxiety Depression Scale (HADS)” are performed. The laboratory data is recorded. The perceived stress related to Covid-19 pandemic and its associations were investigated.The mean age of patients was 44.2±13.3 years, and 65 of the patients (61.3%) were men. Forty-nine (46.2%) of the patients had high-perceived stress; 51 (48.1%) of the patients had poor sleep quality, 40 (37.7%) of the patients had insomnia, 25 (23.6%) of the patients had anxiety and 47 (44.3%) of the patients had depression. The patients having a history of Covid-19 infection in own or closed relatives  and the patients who have a rejection episode any of time  have had significantly higher anxiety scores, when they compared to others. The high PSS were positively correlated with PSQI, ISI, HAD-A and HAD-D. Regression analyses revealed that high-perceived stress is an independent predictor of anxiety and depression. There was not significant difference between kidney function with PSS, PSQI, ISI, HAD-A and HAD-D.High PSS is positively correlated with poor sleep quality and insomnia and also an independent predictor of anxiety and depression in kidney transplant recipients during the outbreak of Covid-19. As the pandemic is still spreading worldwide quickly early identification and intervention of sleep disturbances and psychiatric disorders are essential to protect graft function with high compliance to treatment in transplantation patients. Kidney transplantation is the best option for the treatment of end stage renal disease. Kidney transplant recipients are on life long immunosuppressive drugs which render them to be more vulnerable to infectious diseases such as current Covid-19.The coronavirus disease 2019 (Covid-19), which emerged in China in December 2019, is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: PartlyReviewer #2: No**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: I Don't KnowReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: No**********5. Review Comments to the AuthorPlease 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. Reviewer #1: Is a very interesting paper looking at sleep disturbances and psychological issues in renal transplant recipients during the early phases of Covid 19The authors study is subset of renal transplant recipients early in 2020.This was early in the COVID-19 pandemic.It is unclear what the baseline rate of sleep disturbances and concerns would be in this set of patients. For instance "post holiday blues", other concerns with the renal transplant as well as family concerns etc.Reviewer #2: This paper was aimed at providing an insight into the impact of the current pandemic on psychological well-being and associated sleep quality in kidney transplant recipients. Sleep quality and stress levels were measured by having patients fill out corresponding questionnaires during clinical visits, and the responses were analyzed in combination with laboratory test results. Overall, the paper addresses a novel and interesting question. As is, the paper's message is that high perceived stress is associated with other psychological indicators such as bad sleep and high anxiety. However, without showing that the psychological impact of the pandemic is associated with increased graft loss risk or any other significant outcome in patients, or at least that the increased stress levels contribute to health issues in patients, it has moderate significance.My main concern is the authors need to more clearly show the connection between sleep quality, stress levels and kidney transplant function, in order to make a case for significance of this study. The importance of the study can at least be shown by citing previous research showing the importance of emotional well-being in kidney transplant recipients overall. Though a statement about this connection is made in abstract and introduction, there were no references provided. In addition, whether the stress levels are impacted by the Covid-19 pandemic was also shown, with moderate to weak evidence. Finally, lack of correlation between PSS/PSQL/ISI/HAD readouts and kidney function from laboratory tests, shown in the results, does not add to the importance of the paper. Going into more detailed analysis, it would be very interesting to see any possible association between stress levels and hypertension, whose impact on kidney function is widely recognized. And to make the paper more useful, it would help to identify what exactly about the pandemic  makes kidney transplant recipients prone to psychological disorders.My smaller notes would be about methods description and results presentation. It would be nice to know at which transplant center(s) the patients included in the study were followed-up. It would be also nice to see the breakdown of stress/sleep scores among patients grouped by creatinine/BUN levels. Most of tables should better be re-designed to focus on differences that are significant, rather than showing everything. Table 3 can be presented such that patients are grouped by GFR and/or by measured stress levels. Some statements (such as “psychiatric disorders may lead to noncompliance” and “Outbreaks of infectious diseases and current Covid-19 may trigger…” in introduction) need to be provided with references.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  12 Feb 202112.02.2021Dear Editor in ChiefIn response to your letter dated 29.01.2021, we have revised our manuscript entitled “The Association Between Perceived Stress Related with Sleep Quality, Insomnia, Anxiety and Depression in Kidney Transplant Recipients During the Covid-19 Pandemic” taking into account the reviewer’s comments and proposals. Please find below our itemized response to the points raised by the reviewer.  Please inform us if you require further revision. Thank you for your kind interest. Yours sincerely,Dilek Barutcu Atas, MDExplanations to the Comments and Suggestions Raised by the Reviewer Reviewer1: This paper was aimed at providing an insight into the impact of the current pandemic on psychological well-being and associated sleep quality in kidney transplant recipients. Sleep quality and stress levels were measured by having patients fill out corresponding questionnaires during clinical visits, and the responses were analysed in combination with laboratory test results. Overall, the paper addresses a novel and interesting question. As is, the paper's message is that high perceived stress is associated with other psychological indicators such as bad sleep and high anxiety. 1. However, without showing that the psychological impact of the pandemic is associated with increased graft loss risk or any other significant outcome in patients, or at least that the increased stress levels contribute to health issues in patients, it has moderate significance.Response: We would like to thank the reviewer for the kind comments related to our manuscript. We added a paragraph into the introduction section Line 75-81 as follows “Studies of previous quarantines for SARS, influenza A and Ebola revealed high rates of post-traumatic stress and depression up to 4 to 5 times higher in quarantined subjects (4). It has been shown before that transplant recipients are susceptible to anxiety, depression, and post-traumatic stress disorder (5). Some studies report that post-transplant depression and anxiety increases morbidities and mortality risk of the patients, with poorer medical adherence and/or pathophysiological abnormalities which contribute to poor health outcomes .” And we added a sentence into the introduction section Line 90-91 as follows “Non-adherent patients were seven times more at risk of graft failure than adherent patients (12).2. My main concern is the authors need to more clearly show the connection between sleep quality, stress levels and kidney transplant function, in order to make a case for significance of this study. Response: We would like to thank reviewer for the kind comments related with our manuscript. We completely agree with the reviewer accordingly we added to the Results section line 283-285 as follows “There were no correlations between glomerular filtration rate and PSS , PSQI , ISI , HAD-A  and HAD-D .” And we added to the Discussion section line 412-429 as follows “Previous studies suggest that psychological distress and perceived stress affect compliance negatively and non-compliance results in increased morbidity and mortality in kidney transplant recipients . Although an investigation about sleep quality and its related psychosocial variables among 438 renal transplant patients showed that the global PSQI scores were higher in participants with abnormal renal function compared with participants with normal renal function (35), there was no correlation between kidney function and PSS, PSQI, ISI, HAD-A and HAD-D scores in our study. Re-evaluation in short and long term follow-up of our study subjects during and after the Covid-19 pandemic will better reflect the association between sleep quality, stress levels and kidney transplant function.”3. The importance of the study can at least be shown by citing previous research showing the importance of emotional well-being in kidney transplant recipients overall. Though a statement about this connection is made in abstract and introduction, there were no references provided. Response: We would like to thank reviewer for the kind comments related with our manuscript. We added a sentence into the Introduction section line 97-99 with provided reference as followed “Emotional well-being is important in kidney transplant recipients and improve the treatment compliance and decrease the probability of rejection (17).”4. In addition, whether the stress levels are impacted by the Covid-19 pandemic was also shown, with moderate to weak evidence. Response: We would like to thank reviewer for the kind comments related with our manuscript. We agree with the reviewer and we mentioned this as a study limitation in the Discussion section line 439-441 as follows “Since the patients’ psychological status and sleep conditions before the out-break were not evaluated, it is difficult to infer a causal relationship between the variables of interest and the latter.” 5. Finally, lack of correlation between PSS/PSQL/ISI/HAD readouts and kidney function from laboratory tests, shown in the results, does not add to the importance of the paper. Response: We would like to thank reviewer for the kind comments related with our manuscript. Transplanted patients tend to be more affected from stress disorders, insomnia, and poor sleep quality even before the Covid-19 pandemic. In fact, we investigated in this study, whether PSS/PSQL/ISI/HAD scores were affected more in those with poor kidney function. Unfortunately, we don’t know the impact of psychiatric and sleep disturbances of progression on kidney function related to Covid-19 pandemic in our kidney transplant recipients. We believe that it’s difficult to show correlation between PSS/PSQL/ISI/HAD score and kidney function at the moment. Re-evaluation after a certain time period of Covid-19 pandemic it would be better to show the connection between sleep quality, stress levels and kidney transplant function. 6. Going into more detailed analysis, it would be very interesting to see any possible association between stress levels and hypertension, whose impact on kidney function is widely recognized. Response: We would like to thank reviewer for the kind comments related with our manuscript. We made more detailed analysis to see any possible association between stress levels, sleep disturbances and hypertension, whose impact on kidney function is widely recognized. We added the following paragraph to Results section line 290-293 as follows “When patients with hypertension (n:17) were compared to those without hypertension (n:89); there were no significant differences in PSS , PSQI , ISI , HAD-A  and HAD-D  scores between the two groups.” And we added following paragraph into the Discussion section line 430-437 “To address the impact of hypertension due to increased stress on kidney function we analysed the possible association between stress levels, sleep disturbances and hypertension. However, we failed to show any significant association between stress levels, sleep disturbances and hypertension. Xie et al. showed that the global PSQI scores were higher in the hypertension group compared with those in the non-hypertension group (35). The reason for the lack of association in our study may be due to the scarcity of hypertension among our subjects. Future studies may be needed to show the association after Covid-19 pandemic is over.”7. And to make the paper more useful, it would help to identify what exactly about the pandemic  makes kidney transplant recipients prone to psychological disorders.Response: We would like to thank reviewer for the kind comments related with our manuscript. Unfortunately, we did not ask any questions to identify what exactly about the pandemic makes kidney transplant recipients prone to psychological disorders. However, when we made a literature search we found some possible factors leading to psychiatric disorders in susceptible individuals such as kidney transplant recipients. We added a sentence into the Introduction section line 72-74 as follows “In the pandemic period, restrictions in social life, social isolation, quarantine, boredom, inadequate information, and virus-related fears can lead to various psychiatric disorders in susceptible individuals (3).”8. My smaller notes would be about methods description and results presentation. It would be nice to know at which transplant center(s) the patients included in the study were followed-up. Response: We would like to thank reviewer for the kind comments related with our manuscript. We agree with reviewer. The transplant center is Marmara University. We added following sentence into the Methods and Materials section line 113-114 “The study population was selected among kidney transplant patients followed up at the Marmara University Hospital Transplantation clinic between 01.09.2020 and 01.12.2020.”9. It would be also nice to see the breakdown of stress/sleep scores among patients grouped by creatinine/BUN levels. Response: We would like to thank reviewer for the kind comments related with our manuscript. We have grouped patients by GFR to see the breakdown of stress/sleep scores. We added following paragraph into the Results section line 279-283 as follows “When patients with less than 60 ml/min/1.73 m2 glomerular filtration rate (GFR) were compared to those with GFR>60 ml/min/1.73 m2; there were no significant differences in PSS , PSQI , ISI , HAD-A  and HAD-D  scores between the two groups.”10. Most of tables should better be re-designed to focus on differences that are significant, rather than showing everything. Response: We would like to thank reviewer for the kind comments related with our manuscript. We completely agree with the reviewer and accordingly we have re-designed tables. We deleted Table 2 and re-ordered the statements of tables so Table 4 is named as Table 2. We changed Table 5 as Figure 1 and so Table 6 is named as Table 4. 11. Table 3 can be presented such that patients are grouped by GFR and/or by measured stress levels. Response: We would like to thank reviewer for the kind comments related with our manuscript. We completely agree with the reviewer and accordingly we changed the Table 3, which is stated in the Results section, as patients grouped by measured stress levels high or low.12. Some statements (such as “psychiatric disorders may lead to noncompliance” and “Outbreaks of infectious diseases and current Covid-19 may trigger…” in introduction) need to be provided with references. Response: We would like to thank reviewer for the kind comments related with our manuscript. We provided mentioned statements with references 11 and 16.Reviewer2:Is a very interesting paper looking at sleep disturbances and psychological issues in renal transplant recipients during the early phases of Covid 19The authors study is subset of renal transplant recipients early in 2020.This was early in the COVID-19 pandemic.1. It is unclear what the baseline rate of sleep disturbances and concerns would be in this set of patients. For instance, "post holiday blues", other concerns with the renal transplant as well as family. Response: We would like to thank reviewer for the kind comments related with our manuscript. Transplanted patients tend to be more affected from stress disorders, insomnia, and poor sleep quality even before the Covid-19 pandemic. Unfortunately, we don’t know baseline rate of sleep disturbances and concerns of our kidney transplant recipients. We mentioned this as a study limitation in the Discussion section line 439-441 as followed “Since the patients’ psychological status and sleep conditions before the out-break were not evaluated, it is difficult to infer a causal relationship between the variables of interest and the latter.” And also after a literature search to identify what exactly about the pandemic makes kidney transplant recipients prone to psychological disorders we added a sentence into the Introduction section line 72-74 as follows “In the pandemic period, restrictions in social life, social isolation, quarantine, boredom, inadequate information, and virus-related fears can lead to various psychiatric disorders in susceptible individuals (3).” and We added a paragraph to show association the psychological impact of the pandemic and health conditions such as graft loss into the introduction section Line 75-81 as follows “Studies of previous quarantines for SARS, influenza A and Ebola revealed high rates of post-traumatic stress and depression up to 4 to 5 times higher in quarantined subjects 4. It has been shown before that transplant recipients are susceptible to anxiety, depression, and post-traumatic stress disorder 5. Some studies report that post-transplant depression and anxiety increases morbidities and mortality risk of the patients, with poorer medical adherence and/or pathophysiological abnormalities which contribute to poor health outcomes 6,7. We completely agree with the reviewer that it’s important to show "post holiday blues" and other concerns with kidney transplant recipients and as well as their family. We believe future studies may reveal the impact of pandemic on physiological and sleep disorders and its association between morbidity and mortality in kidney transplant recipients.Journal Requirements1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. Response: We would like to thank editor for the kind comments related with our manuscript. We have checked our manuscript for PLOS ONE's style requirements.2. Thank you for your ethics statement: "The investigation conforms with the principles outlined in the Declaration of Helsinki. The local ethics committee approved the study, and all participants gave written informed consent (Protocol number: 09.2020.991)."Please amend your current ethics statement to include the full name of the ethics committee/institutional review board(s) that approved your specific study.Once you have amended this/these statement(s) in the Methods section of the manuscript, please add the same text to the “Ethics Statement” field of the submission form (via “Edit Submission”).Response: We would like to thank editor for the kind comments related with our manuscript. We have edited our current ethics statement as follows “The study design was approved by the institutional review board of Marmara University School of Medicine Ethic Committee and all participants gave written informed consent. (Protocol number: 09.2020.991).”AttachmentResponse to Reviewers Comments.docxSubmitted filename: Click here for additional data file. 22 Feb 2021The association between perceived stress with sleep quality, insomnia, anxiety and depression in kidney transplant recipients during Covid-19 pandemicPONE-D-20-40967R1Dear Dr. Atas,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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For more information, please contact Kind regards,Stanislaw StepkowskiAcademic EditorPLOS ONEAdditional Editor Comments :NoneReviewers' comments: 24 Feb 2021PONE-D-20-40967R1 The Association Between Perceived Stress with Sleep Quality, Insomnia, Anxiety and Depression in Kidney Transplant Recipients During Covid-19 Pandemic Dear Dr. Barutcu Atas: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. onepress@plos.org.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 plosone@plos.org. 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Postpartum preeclampsia is a serious disease related to high blood pressure that occurs commonly within the first six days after delivery. Methodology. A randomized, single-blind longitudinal clinical trial of 42 puerperal patients with severe preeclampsia was carried out. Patients were randomized into two groups: the experimental group (n = 21) received diltiazem (60 mg) and the control group (n = 21) received nifedipine (10 mg). Both drugs were orally administered every 8 hours. Systolic, diastolic, and mean blood pressures as well as the heart rate were recorded and analyzed (two-way repeated measures ANOVA) at baseline and after 6, 12, 18, 24, 30, 36, 42, and 48 hours. Primary outcome measures were all the aforementioned blood pressure parameters. Secondary outcome measures included the number of hypertension and hypotension episodes along with the length of stay in the intensive care unit.  To evaluate if diltiazem improves blood pressure parameters in early puerperium patients with severe preeclampsia.  No statistical differences were found between groups (diltiazem vs. nifedipine) regarding basal blood pressure parameters. Interarm differences in blood pressure  and heart rate were statistically significant between treatment groups from 6 to 48 hours. Patients in the diltiazem group had lower blood pressure levels than patients in the nifedipine group. Significantly, patients who received diltiazem had fewer hypertension and hypotension episodes and stayed fewer days in the intensive care unit than those treated with nifedipine. NCT04222855. Diltiazem controlled arterial hypertension in a more effective and uniform manner in patients under study than nifedipine. Patients treated with diltiazem had fewer collateral effects and spent less time in the hospital. This trial is registered with  Both preeclampsia and eclampsia are important health problems, being important causes of maternal death worldwide –3. AlthoSevere preeclampsia is defined as “artery pressure greater than or equal to 160/110 mmHg in at least two determinations with less than two hours of difference. Patients may present headache, acute lung edema, blurred vision, phosphenes, right flank pain, vomiting, liver hypersensitivity, HELLP syndrome, thrombocytopenia, and elevated liver enzymes (ALT or AST)” [Antihypertensive drugs in pregnancy, including oral nifedipine (10 mg), methyldope (250 mg), labetalol (100 mg i.v.), and furosemide (20 mg OD), are used during postpartum too –14.Arterial hypertension that accompanies severe preeclampsia is caused by an increase in peripheral vascular resistance. For this reason, calcium antagonists are ideal candidates for antihypertensive treatment in these patients. –17.Nifedipine has been used as the drug of choice for the treatment of hypertension during puerperium for more than 25 years , 17. HowDiltiazem is an alternative calcium antagonist that is 1000 times less potent than nifedipine, which has the property of producing a selective vasodilatation of arteries beds –25. ThusA randomized, single-blind, longitudinal clinical study was carried out with patients in early puerperium (defined as the first 24 hours after delivery) , 27 withNCT04222855.All patients signed an informed consent prior to their participation on the study. The study is registered in ClinicalTrials.gov with identifier number A researcher not involved in the study performed the randomization by using a random-numbers table and central allocation. Treating study physicians, nurses, and study investigators were not blinded to group assignment, whereas study participants were blinded to treatment allocation. Patients were randomly allocated to one of the two treatment groups: the group one was administered 60 mg of diltiazem (tables), while the group two (the control) was administered 10 mg of nifedipine (capsule). Both drugs were given orally every 8 hours.Primary outcome measures were blood pressure parameters. Systolic, diastolic, and mean blood pressures and heart rate were recorded at baseline and after 6, 12, 18, 24, 30, 36, 42, and 48 hours. Secondary outcome measures included the number of hypertension and hypotension episodes and ICU (intensive care unit) length of stay.A Foley catheter was placed to measure urinary excretion. Also, to continuously measure the blood pressure of all patients, a catheter was placed in the radial artery and recorded with a monitor . It provided readings of systolic, diastolic, and mean blood pressure. Readings were taken during the 48-hour observation period in order to ensure that blood pressure was maintained between 120/70 and 160/110 mmHg, and to either determine hypotension  or hypertension events, according to a Cochrane systematic review . For patAn electrocardiogram was performed to determine the heart rate. A solution was administered at the rate of 100 ml/hr to liquid handling, and this flow was adjusted according to central venous pressure measurements and laboratory tests results.t-test formula. For calculation, we used systolic pressure values derived from a pilot study of 10 subjects for each group. The common standard deviation of delta values (pressure differences between baseline and after 72-hour) is 19 mmHg, difference between delta values of treatments is 16.5 mmHg, and power level of 0.80, resulted in 21 patients per group.Sample size was determined by using two-independent samples Descriptive statistics  were used to summarize the clinical and biochemical features of the study sample.t-test was used to compare both treatments (diltiazem and nifedipine) on the arterial blood pressure variables. In addition, pregnancy and maternity characteristics and liver and kidney function variables were analyzed using this test. Qualitative variables were compared using Fisher's exact test. All analyses employed IBM Statistics SPSS 21, while line graphs of two sets of group data were done in Sigma Plot. Statistical significance was reached at p < 0.05.To explore if the two groups were homogeneous at baseline, Student's Comparisons between groups on the repeated measurements were made using repeated measures ANOVA . AnalyseAdditionally, we fitted a binomial negative model to estimate the average number of hypotension episodes on days of hospitalization and incidence rate ratio. After fitting the model, residual deviance was used to perform a chi-square goodness of fit test for the overall model , 31.The trial flow diagram of patient enrollment is depicted in Baselines characteristics of both groups were homogeneous  with resDescriptive data of outcome measures by treatments and time point for blood pressure variables analyses are presented in Tables p=0.63) and baseline diastolic blood pressure (DBP) (112.6 (5.5) vs. 111.2 (7.6), p=0.49). At six hours, the difference of SBP (133.4 (10.2) vs. 147.9 (9.7)) and DBP (78.5 (7.7) vs. 90.6 (5.5)) was statistically significant between groups . The trends of differences were evident in the following evaluations of both systolic pressure and diastolic pressure between diltiazem and nifedipine treatments (p=0.62), and from six until 48 hours, the average of MAP was statistically significant between treatments  showed that, in both study arms (diltiazem and nifedipine), there was no difference in baseline systolic blood pressure (SBP) (156.2 (13.6) vs. 158.3 (14.0), eatments . Similareatments .p=0.13) and at hour 6 was 93.8 (13.8) vs. 98.8 (12.2), (p=0.22); therefore, the difference was not statistically significant between the diltiazem and nifedipine groups. At all other times of heart rate recordings, the difference between groups was statistically significant . Whereas only 3 (14.3%) patients suffered hypotension episodes in the diltiazem group, and 15 (71.4%) patients suffered episodes of the same condition in the nifedipine group. On one hand, there were no hypertension episodes in the diltiazem group, which contrasts with 7 (33.3%) patients who suffered episodes of this condition in the nifedipine group.Analysis of secondary outcome measures was performed to compare collateral effects and the number of hypotension and hypertension episodes. The number of hypotension and hypertension episodes was statistically significant between groups  regression model to estimate the average predicted count of hypotension episodes between treatments, which resulted in 0.238 and 2.095 in the diltiazem and nifedipine groups, respectively. Regression coefficients and standard error of the model are presented in p < 0.001). Thus, the total number of days in intensive care is lower in the diltiazem group, and these data could be used to estimate potential savings in this emergency therapy.Finally, patients in the diltiazem group spent an average of 2.47 (0.60) days in intensive care, while patients in the nifedipine group spent an average of 4.57 (1.36) days  less days in intensive care. Hence, it can be inferred that by decreasing the length of stay in the intensive care unit , costs would be favorably affected. However, a pharmacoeconomic analysis is necessary for evaluating this consideration.Our study has some limitations. Providers were not blinded to treatment allocation. Also, we did not collect information on blood pressure parameters after discharge from the ICU. It would also have been interesting in conducting a longer observation of patients.Administration of diltiazem (60 mg) was shown to be effective for controlling blood pressure in early puerperium patients with severe preeclampsia, leading to a more uniform control of arterial hypertension compared to nifedipine. Reduction in blood pressure was significantly greater in the diltiazem group than that in the nifedipine group during the 48-hour observation period. Finally, the improved blood pressure control produced with diltiazem was accompanied by less adverse episodes and less time in the intensive care unit.


In many low- and middle-income countries, where vaccinations will be delayed and healthcare systems are underdeveloped, the COVID-19 pandemic will continue for the foreseeable future. Mortality scales can aid frontline providers in low-resource settings (LRS) in identifying those at greatest risk of death so that limited resources can be directed towards those in greatest need and unnecessary loss of life is prevented. While many prognostication tools have been developed for, or applied to, COVID-19 patients, no tools to date have been purpose-designed for, and validated in, LRS.This study aimed to develop a pragmatic tool to assist LRS frontline providers in evaluating in-hospital mortality risk using only easy-to-obtain demographic and clinical inputs.Machine learning was used on data from a retrospective cohort of Sudanese COVID-19 patients at two government referral hospitals to derive contextually appropriate mortality indices for COVID-19, which were then assessed by C-indices.with pulse oximetry, oxygen saturation is included and in settings without access, heart rate is included. The AFEM-CMS showed good discrimination: the model including pulse oximetry had a C-statistic of 0.775 (95% CI: 0.737–0.813) and the model excluding it had a C-statistic of 0.719 (95% CI: 0.678–0.760).Data from 467 patients were used to derive two versions of the AFEM COVID-19 Mortality Scale (AFEM-CMS), which evaluates in-hospital mortality risk using demographic and clinical inputs that are readily obtainable in hospital receiving areas. Both versions of the tool include age, sex, number of comorbidities, Glasgow Coma Scale, respiratory rate, and systolic blood pressure; in settings In the face of an enduring pandemic in many LRS, the AFEM-CMS serves as a practical solution to aid frontline providers in effectively allocating healthcare resources. The tool’s generalisability is likely narrow outside of similar extremely LRS settings, and further validation studies are essential prior to broader use. Initial reports suggested that low- and middle-income countries (LMICs), whose health systems are generally categorised as underdeveloped , would f25In LMICs, only 20% of the population is forecasted to receive vaccination against COVID-19 this calendar year , and cas13151617This study aimed to develop a pragmatic tool to assist facility-based healthcare providers in LRS in evaluating in-hospital mortality risk using only demographic and clinical inputs that are readily obtainable upon arrival in EUs.Machine learning was used on data from a retrospective cohort of Sudanese COVID-19 patients to derive a contextually appropriate mortality scale for COVID-19. Tool derivation and reporting followed the Transparent Reporting of a Multivariable Prediction Model for Individual Prediction or Diagnosis (TRIPOD) guidelines [The Republic of the Sudan, Africa’s third largest nation, is home to 41.8 million . The cou2020Existing resource constraints have delayed implementation of an electronic COVID-19 patient registry in Sudan, and most facilities continue to rely on traditional paper-based charting systems. For the purposes of this study, on-site data collectors retrospectively logged deidentified historical and clinical information for patients presenting to the two study sites from paper records into a secure electronic Microsoft Excel  database stored locally. Data were randomly split into two cohorts—80% for training and 20% for testing—for derivation purposes.All patients were adults 18 years and over who presented to study sites between 01 April to 01 September 2020; met WHO criteria for a suspected, probable, or confirmed case of SARS-CoV-2 infection ; and hadThe primary outcome of this study was in-hospital mortality due to SARS-CoV-2 infection.a priori based on a review of validation data for previously designed pneumonia and sepsis scores, and purpose-designed COVID-19 indices, in COVID-19 populations (Appendix 1). This list was generated in the context of the results of two studies—a scoping review and systematic review and meta-analysis—that evaluated the feasibility and significance of a range of variables in predicting severe COVID-19 in LRS [A set of potentially predictive variables was defined 9 in LRS 23. Thesea priori decision was made to include peripheral oxygen saturation in model development but, should it be included in the final scale, to use identical methods to develop a second scale using identical methodology that does not consider this as a potential input.Available literature suggests that demographics, comorbidities, signs and symptoms, vital signs, and blood glucose testing can feasibly be collected in most LRS EUs, while laboratory investigations and imaging generally are not 1718. Per17241626Where evidence was unavailable to support inclusion, variables were excluded. Variables with greater than 33% missingness were also excluded. Comorbidities were collapsed into clinically relevant categories by local clinicians. These categories were viewed as binary, with patients either having one or more of the included comorbidities, or none.Table 1).Given the limited size of this dataset, we elected to use the following predefined parameters for continuous predictors included in the final model (Multiple imputation by chained equations (MICE) was used for predictors in instances where less than 33% of data points were missing. Data were assumed to be missing completely at random. A total of 10 iterations of MICE were conducted, with predictive mean matching as the designated method for continuous variables and binary logistic regression for two-level variables. Distributions of missing and observed data were reviewed for each predictor, and Rubin’s rules were used to combine results .A criterion-based method of model selection, similar to that used by Knight and colleagues , was utiLeast absolute shrinkage and selection operator (LASSO) logistic regression on a multiply-imputed dataset was used for coefficient estimation. LASSO was selected to prevent overfitting due to the large number of predictor variables. A 10-fold cross-validation was performed to identify the optimal value of lambda, from which L1 penalised coefficients were generated. These L1 penalised coefficients were then proportionally scaled to create a usable scoring scale for the AFEM-CMS. Scatter plots distributions comparing scores and case fatality rates were visually inspected by local clinicians to determine clinically meaningful cut-off points for risk groups.Model performance in the derivation dataset was assessed using the C-index (area under the receiver operator curve (AUROC)) in the testing dataset.dplyr, finalfit, glmnet, mice, pROC, rmda, and tidyverse packages.All analyses were performed in R  using the Table 2). Only one third of the cohort (n = 177) had recorded results for polymerase chain reaction (PCR) testing, of which 75.1% were positive. All remaining patients were diagnosed via chest CT scan. All patients met the WHO case definition for suspected, probable, or confirmed SARS-CoV-2 infection [A total of 467 patients met the inclusion criteria and were included in this study .Case fatality rates varied over time, with peaks near 70% in April and September 2020 and a low point of 41.2% in July 2020 . An additional five presenting signs and symptoms and one comorbidity were removed due to lack of evidence supporting inclusion in a COVID-19 risk stratification tool. Comorbidities were collapsed into a composite variable reflecting the patient’s total number of comorbidities. Inclusion decisions for model fitting, including Table 3a).Seventeen variables were included in the model fitting process, which used GAMs on the multiply imputed datasets to identify significant independent predictors of COVID-19 mortality. Male sex at birth, age, number of comorbidities, total number of comorbidities, GCS, systolic blood pressure, respiratory rate, and peripheral oxygen saturation on room air were found to be predictive (Appendix 2). LASSO regression was used to generate a usable scoring system, with all variables retained in the model (Appendix 3 and Table 3b).In line with this study’s aim to generate a flexible but pragmatic tool for LRS, a second analysis was run excluding peripheral oxygen saturation as a predictor. In this analysis, seven variables—male sex at birth, age, number of comorbidities, total number of comorbidities, GCS, systolic blood pressure, respiratory rate, and heart rate—were identified as important predictors of mortality (Appendix 2) and included in a second scale that is not reliant on pulse oximetry  led to the designation of clinically meaningful cut-off points that categorised patients into three mortality risk groups, as defined in Table 5 and Figures 2a and 2b.In both versions of the scale, most patients—47.3% (n = 221) in the tool with SPO2 and 57.2% (n = 267) in the one without—were estimated to have between 33% and 66% risk of death. Distributions of scores are shown in Both versions of the mortality scale showed good discrimination. The model including pulse oximetry had a C-statistic of 0.780 (95% CI: 0.740–0.820) in the training set and 0.775 (95% CI: 0.737–0.813) in the testing set. For the model excluding pulse oximetry, the C-statistic was 0.732 (95% CI: 0.686–0.778) in the training set and 0.719 (95% CI: 0.678–0.760) in the testing set.This study led to the successful generation of the AFEM-CMS, a pragmatic mortality scale purpose-designed for frontline clinicians in extremely LRS. Two versions of the tool exist, each using seven demographic, historical, and clinical inputs to evaluate potential risk of death in COVID-19 patients. The tool is intended for frontline use and, as such, does not include clinical inputs that may take significant time to obtain or may be completely unavailable in LRS EUs, such as laboratory and imaging investigations. One version of the AFEM-CMS does, however, include pulse oximetry, which has been found to be a strong predictor of poor outcomes in COVID-19 patients but has variable availability in LMICs 24252627.242526Data from 34 independent predictors were available; however, two of these were not included in model fitting due to excessive missing data points and another six due to lack of evidence that they are predictive of mortality in COVID-19 disease. Presenting signs and symptoms accounted for 8 of the 17 variables considered in our model. Despite evidence suggesting that these variables may be predictive of severe disease, no signs or symptoms were included in the final model. The case was the same for smoking, which has been identified in multiple meta-analyses as predictive of disease progression.The AFEM-CMS shares many similarities with existing risk stratification tools for COVID-19 but is unique in its combination of variables and weightings. Similar to many other COVID-19 specific tools, such as A-DROP  and the In the version of the AFEM-CMS including pulse oximetry measurements, all vital signs except heart rate were included in the final model. In the absence of oxygen saturation measurements, however, heart rate became significantly predictive. Heart rate is likely serving as a proxy for decreased oxygenation in the model without pulse oximetry, as it will typically increase to compensate for decreasing blood oxygen levels. Three other vital signs are included in both models: systolic blood pressure, GCS, and respiratory rate. Decreases in systolic blood pressure and consciousness are late indicators of clinical deterioration and indicate increased potential for mortality. Respiratory rate has been characterised as a key predictor in nearly all existing COVID-19 risk stratification scores, and although cut-points for significance have varied, most tools consider rates greater than approximately 20 breaths per minute as abnormal. We originally sought to stratify patients into three classes—<20, 20–29, and ≥30—as is seen in the 4C Mortality Score; however, the penalised coefficients for the 20–29 and ≥30 classes were extremely similar and generated the same weighting in both models. This led to our tools having only one cut-point for respiratory rate.We sought to generate a scale that categorised patients into one of three clinically meaningful levels of mortality risk. Most three-level mortality indices for COVID-19 and pneumonia described in the literature use standardised mortality-risk cut points to classify patient severity 293536. F2935The Sudanese population was facing healthcare and political crises even before the pandemic, and COVID-19 has only worsened health and economic statuses 37. The hData from this study build upon, and largely align with, the one previous cohort study conducted on COVID-19 patients in Sudan. Omar and colleagues evaluated 88 COVID-19 patients at a Sudanese government hospital in Eastern Sudan between April and July 2020 . As in oThere are several limitations to this study, the most important of which is the generalisability of its results. CFRs varied over time throughout this study, with an initial decrease followed by an increase to the original rate. The AFEM-CMS was developed based on the average of these rates and may perform differently when CFRs are consistently higher or lower. Additionally, given the urgency of the pandemic, only a small dataset could be obtained from just two facilities that had the capacity to participate in this work. These patients were extremely ill and tended to be older than Sudan’s general population. As such, the AFEM-CMS’s generalisability is likely very limited outside of similar extremely LRS settings, such as those nations listed on the Development Assistance Committee’s list of Least Developed Countries , and itsThere is also potential that use of these two facilities’ datasets may have inflated CFRs to rates higher than seen more broadly in Sudan, which are reported to be closer to the rates reported by Omar and colleagues. The hospitals included in this study are referral hospitals, whose advanced care attracts sicker patients whose infections may be more likely to be fatal. To evaluate this further, future validation work will also include data from lower-level facilities.All patients included in this study met the WHO definitions of suspected, probable, or confirmed COVID-19 infection; however, only one third of patients received PCR testing to confirm infection. Although this deficit in testing aligns with previously described testing challenges in LMICs 6, it doeSome self-reported information, including age, specific comorbidities, and presenting signs and symptoms, may have gone undocumented, especially in instances where patients were very ill at the time of presentation. In the Sudanese setting, where paper records are used at government facilities, it is nearly impossible to link to patients’ prior medical records to obtain historical information, and clinicians must rely solely on the narrative interview data gathered at the time of information. This information was missing at slightly higher rates in the non-survivor cohort but does not appear to be extremely different than in the survivor cohort, suggesting that such scenarios were uncommon and would not significantly influence results.Development of the AFEM-CMS may also be limited by the use of predefined parameters for continuous clinical measurements. Although these cut-off points have been established and validated across a number of other risk stratification tools, including some for COVID-19, they may not be reliable for use in LRS. In future studies, analysis of the local distribution of these parameters will be undertaken to ensure that the current cut-off points are ideal.To our knowledge, the AFEM-CMS is the first purpose-designed tool for mortality prediction of COVID-19 patients by frontline providers in extremely LRS. It is intended for rapid use at the frontlines of the response in EUs and other receiving areas of healthcare facilities where laboratory and imaging investigations are limited or unavailable. In the face of an enduring pandemic in many LMICs, the tool could serve as a practical solution to aid frontline providers in effectively allocating limited healthcare resources. However, the tool’s generalisability is likely limited outside of similar LRS with similarly high fatality rates. Future work will be undertaken to validate the tool and to compare it to other scales that may be feasible in LRS as more data become available.Data are available on reasonable request.The additional file for this article can be found as follows:10.5334/aogh.3278.s1Appendices.Appendix 1 to 4.


Background: Two types of global glomerulosclerosis, glomerular obsolescence and solidification, have been identified. A clinicopathological correlation between these glomerular changes and hypertensive nephrosclerosis has been reported; however, clinicopathological correlations with other kidney diseases are unknown. The aim of this study was to evaluate the correlation between the two glomerulosclerosis types and the clinical IgA nephropathy presentation.Methods: A single center, cross-sectional study of patients with IgA nephropathy was performed. Correlations between glomerulosclerosis and body mass index, mean blood pressure, creatinine-based estimated glomerular filtration rate (eGFR), total cholesterol, urinary protein corrected by urinary creatinine, and anti-hypertensive agent use were investigated using univariate and multivariate analyses.Results: Overall, 116 patients were enrolled . Separate analyses were performed for solidification and obsolescence glomerulosclerosis. Univariate analysis demonstrated a significant correlation between the percentage of solidification glomerulosclerosis and patient age, mean blood pressure, eGFR, and use of antihypertensive drugs. Multivariate analysis showed that only eGFR and use of antihypertensive drugs maintained their independent predictive value. The amount of urinary protein emerged as a significant factor based on the multivariate analysis. However, although the univariate analysis demonstrated a statistically significant correlation between the percentage of obsolescence and eGFR for obsolescence glomerulosclerosis, a multivariate analysis indicated that none of the factors maintained their independent predictive value.Conclusions: The incidence of solidification was better correlated with some nephritis-related clinical parameters compared with the incidence of obsolescence. The emergence of solidification may influence the clinical activities that are associated with IgA nephropathy. IgA nephropathy is the most prevalent primary chronic glomerular disease worldwide. It is defined immunohistochemically by a predominance of IgA deposits, either alone or in conjunction with IgG, IgM, or both, in the glomerular mesangium . HistoloTwo types of glomerular sclerosis, glomerular obsolescence and solidification, were originally described in the 1960s. Since then, differences in the features of obsolescent and solidified glomerulosclerosis have been reported in hypertensive nephrosclerosis by Marcantoni et al. ,5. TheseThese two types of global glomerulosclerosis have completely different appearances and seem to be induced by different pathogenic mechanisms. Clinicopathological correlations between these glomerular changes and hypertensive nephrosclerosis have been reported; however, clinicopathological correlations in other kidney diseases, such as IgA nephropathy, remain to be clarified. In this study, we observed obsolescent and solidified glomerulosclerosis in IgA nephropathy biopsies and we evaluated potential clinical pathological correlations.n = 116) were enrolled. The diagnosis was made using light microscopy and immunofluorescence. Patients for whom the biopsy included fewer than 10 glomeruli were excluded from the analysis. All biopsy specimens with periodic acid-Schiff (PAS) staining were independently reevaluated by the authors YL and HO. The presence of the two types of global glomerulosclerosis was assessed and quantified as a percentage of the total number of glomeruli for each biopsy. Results were obtained as the average of the data from both YL and HO. Before histologic evaluation of IgA nephropathy, several hypertensive nephrosclerosis tissue specimens were used to confirm mutual recognition of the characteristics of two types of global glomerulosclerosis. The method described above was based on a previous report [This was a single center, cross-sectional, observational study. Subjects were enrolled for 5 years from October 2009 until September 2014. During this period, 400 renal biopsies were performed and patients aged ≥16 years with biopsy-proven IgA nephropathy (2) = body weight/body height2; mean blood pressure  = diastolic pressure + (systolic pressure – diastolic pressure)/3; creatinine-based estimated glomerular filtration rate (eGFR) calculated using the Modification of Diet in Renal Disease (MDRD) formula for Japanese [2) = 194 × Cr−1.094 × Age−0.287 ; total cholesterol (mg/dL), urinary protein corrected by the urinary creatinine (g/1.0 g urinary creatinine); and the use of any anti-hypertensive agents.Clinical information was obtained from electronic health records and data for the following parameters at the time of admission for renal biopsy were evaluated: body mass index  at our institution, in accordance with the Declaration of Helsinki and the Japanese guidelines for clinical research, approved this research and its publication. This manuscript was originally designed in 2015, but there was a subsequent revision of the guidelines in Japan in 2017. We reevaluated our research plan and method based on these new guidelines, and the study now complies with the new guidelines and was approved by our IRB .p < 0.05 was considered statistically significant.Initially, univariate linear regression was used to assess the associations between the percentage of solidified glomeruli or the percentage of obsolescent glomeruli (dependent variables) and each clinical parameter (independent variables). A multivariate linear regression analysis was then performed to avoid overestimation of potentially linked variables. Multicollinearity was evaluated by the variance inflation factor. To compare two groups, Fisher’s exact test or the Wilcoxon signed-rank test were used. JMP version 10.0  and EZR version 1.20   softwareThe patient characteristics at biopsy are summarized in p = 0.016, 0.042, 0.002, and 0.001, respectively). A multivariate analysis showed that only eGFR and the use of antihypertensive drugs maintained their independent predictive value. The amount of urinary protein emerged as an important factor based on the multivariate analysis for solidified glomeruli. The whole model, including eGFR, urinary protein, and use of antihypertensive drugs, was also a significant predictive model for the percentage of solidified glomeruli  to predict the prognosis of IgA nephropathy in a longitudinal study. We are planning a catamnestic observation over the next 5–10 years to examine this possibility. The two types of sclerosed glomeruli might represent different stages of the same process, which cannot be completely ruled out. To resolve this issue, a prospective multicenter clinical study with a larger sample size that includes patients who underwent a re-biopsy is required in the future. Moreover, cystatin C-based GFR estimation may be better than using creatinine-based eGFR for this study because most subjects did not have severe renal insufficiency.In conclusion, two types of global glomerular sclerosis, glomerular obsolescence and solidification, were observed in IgA nephropathy patients, which is similar to previous reports in patients with hypertensive nephrosclerosis. Additionally, the global sclerosis showed significantly different properties with regard to their association with clinical parameters. Based on our results, glomerular solidification might be more essential than glomerular obsolescence for the progression of IgA nephropathy. Because of the heterogeneity of the clinical course, risk assessment for the development of ESRD is necessary to consider how to balance the potential benefits and risks of treatment. It might be useful to distinguish between glomerular obsolescence and solidification for this purpose.


Patients undergoing cerebral bypass surgery are prone to cerebral hypoperfusion. Currently, arterial blood pressure is often increased with vasopressors to prevent cerebral ischaemia. However, this might cause vasoconstriction of the graft and cerebral vasculature and decrease perfusion. We hypothesised that cardiac output, rather than arterial blood pressure, is essential for adequate perfusion and aimed to determine whether dobutamine administration resulted in greater graft perfusion than phenylephrine administration.This randomised crossover study included 10 adult patients undergoing cerebral bypass surgery. Intraoperatively, patients randomly and sequentially received dobutamine to increase cardiac index or phenylephrine to increase mean arterial pressure (MAP). An increase of >10% in cardiac index or >10% in MAP was targeted, respectively. Before both interventions, a reference phase was implemented. The primary outcome was the absolute difference in graft flow between the reference and intervention phase. We compared the absolute flow difference between each intervention and constructed a random-effect linear regression model to explore treatment and carry-over effects.−1) after dobutamine administration and 3.6  ml min−1 after phenylephrine administration . There was no treatment effect  and no carry-over effect.Graft flow increased with a median of 4.1 .Netherlands Trial Register, NL7077 ( Thus, we aimed to study the effect dobutamine administration This randomised crossover study was conducted between September 2018 and July 2019 at the University Medical Centre (UMC) Utrecht in adherence to the Consolidated Standards of Reporting Trials (CONSORT) statement: extension to randomised crossover trials.Adult patients (≥18 yr) presenting for an extracranial–intracranial or intracranial–intracranial cerebral bypass were eligible for inclusion after written informed consent was obtained, irrespective of the indication or type of graft. Exclusion criteria were an emergency procedure, pregnancy, a contraindication for either dobutamine or phenylephrine, and MAP <60 mm Hg or SBP >180 mm Hg under general anaesthesia before the start of the interventions . Patient−1 min−1) and phenylephrine (0.15–1 μg kg−1 min−1) varied depending on their effect on cardiac index and blood pressure, respectively. For dobutamine, the infusion rate was targeted at an increase in cardiac index of at least 10%, as compared with the mean cardiac index in the reference phase. For phenylephrine, the infusion rate was adjusted to target a 10% increase in MAP as compared with the reference phase. During the reference and the intervention phases SBP, MAP, heart rate, stroke volume, cardiac index, and graft flow were measured every minute once a steady state was reached for at least 2 min.The interventions took place after construction of the bypass. Patients were randomised to sequentially receive dobutamine and phenylephrine via a central venous catheter . RandomiPaco2), no adjustments to ventilator settings were allowed and end-tidal carbon dioxide (ETCO2) values were documented throughout the study period. To prevent vasoconstriction, topical application of papaverine at the intracranial part of the bypass was allowed throughout the measurement period and at the discretion of the neurosurgeon.Infusion of fluids and administration of other medications was kept constant throughout the study period. To maintain a constant arterial carbon dioxide pressure , and uncalibrated arterial pressure waveform analysis was used to measure stroke volume and cardiac index.The anaesthesiologist was blinded for graft flow, whereas the neurosurgeon, who measured graft flow, was blinded for medication given and blood pressure and cardiac index data.−1, maintenance infusion 4–10 mg kg−1 h−1), remifentanil (0.25–0.5 μg kg−1 min−1), and atracurium . Management of blood pressure and ETCO2 outside the study period were left to the judgement of the attending anaesthesiologist, except that our local protocol prescribed to maintain SBP <180 mm Hg and to keep ETCO2 between 4.7 and 6.0 kPa. During anaesthesia but outside the study period, episodes of hypotension were treated with ephedrine  or phenylephrine . When phenylephrine infusion was started before the start of the study, this was continued at a constant infusion rate during the study period. The intervention drug, either dobutamine or phenylephrine, was given in addition to this maintenance infusion. Only during the wash-out period, was a bolus or change in maintenance infusion of phenylephrine allowed and this was left to the judgement of the attending anaesthesiologist.General anaesthesia was maintained with propofol  or medians with 25th and 75th percentiles (inter-quartile range [IQR]) as appropriate.All analyses were performed with use of R .The mean graft flow was estimated for each reference and intervention phase and was plotted over time for each patient. The change in graft flow between intervention phase I and the corresponding reference phase I, and between intervention phase II and reference phase II was calculated. Afterwards, a two-sided Wilcoxon signed rank test was used to assess the difference in flow, after confirmation that the data were not normally distributed. A pseudo-median was reported as differences between paired samples were not fully symmetrically distributed around the median. The same method was applied to assess differences in MAP, SBP, heart rate, stroke volume, and cardiac index.vs phenylephrine), the sequence effect (i.e. randomisation order), and a potential carry-over effect (i.e. the duration of the wash-out period), a random-effect multivariable linear regression model was constructed, with graft flow as the dependent variable and treatment (dobutamine vs phenylephrine) as a fixed effect. Subject ID was included as a random effect to account for within-subject variance. As graft flow may increase between opening of the bypass and the end of surgery,To study the treatment effect  were reported. There was no need to account for multiplicity.vice versa  min and 0.31  μg kg−1 min−1 phenylephrine for 12 (10–14) min were administered. Graft flow increased with a median of 4.1  ml min−1 after administration of dobutamine and with 3.6  ml min−1 after administration of phenylephrine . Cardiac index increased with a median of 1.1  L min−1 m−2 during dobutamine administration, whereas MAP decreased with a median of –7  mm Hg. MAP increased with a median of 16  mm Hg during phenylephrine administration, which was not accompanied by a change in cardiac index  L min−1 m−2. In all patients, ETCO2 was kept constant . There was no carry-over effect for dobutamine .A median of 4.1  μg kglephrine . The pseconstant . The chaIn one patient, distinctive results were found. This patient was included twice as she presented for a second, contralateral bypass. During the second procedure we found a very low graft flow, with hardly any effect on graft flow during both an increase in MAP and cardiac index . A CT anWe administered dobutamine and phenylephrine to increase cardiac output and blood pressure, respectively, during cerebral bypass surgery and observed similar increases in graft flow. This finding was consistent in most patients and except for a short episode of atrial arrhythmia in one patient, no adverse effects were observed.,When assessing the effect of blood pressure and cardiac output on cerebral perfusion, studies have differentiated between awake and anaesthetised patients and between healthy patients and patients with a disturbed cerebral autoregulation. A study in healthy patients under general anaesthesia found that an increase in MAP with phenylephrine caused a decrease in cerebral oxygenation as measured with near-infrared spectroscopy (NIRS), whereas the cardiac output did not change.12,However, results from studies in awake and healthy subjects may not always apply to the diseased population under general anaesthesia. Anaesthesia can potentially affect cerebral perfusion via a variety of pathways, including suppression of the sympathetic nervous system.Healthy subjects are likely to have good cerebral autoregulation, whereas the effects of adrenergic agents may be stronger in patients with disrupted cerebral autoregulation.,vs a high target MAP (70–80 mm Hg) during cardiopulmonary bypass with a fixed bypass flow found no difference in new ischaemic cerebral lesions on MRI.The differential effects of blood pressure and cardiac output on cerebral blood flow in the neurosurgical population is insufficiently studied. Interestingly, cerebral dysregulation is a phenomenon also seen cardiac surgery.,,,Although cerebral bypass surgery aims to prevent future ischaemic strokes, patients are at risk for perioperative cerebral ischaemia.,,1-receptor agonist phenylephrine, might actually cause cerebral vasoconstriction.35Both α-adrenergic and β-adrenergic receptors are suggested to play a role in cerebral autoregulation.1-, α2-and β1-receptor agonist, primarily causes vasoconstriction and an increase in blood pressure, but can also (slightly) increase cardiac output and may be a good alternative to phenylephrine.We cannot definitively conclude that dobutamine administration benefits all patients presenting for cerebral bypass surgery. Still, this study does show that dobutamine can increase cerebral perfusion and should be considered when targeted graft flows are not reached or only at the cost of (severe) systemic hypertension, when using phenylephrine. Inotropes such as dobutamine can cause arrhythmia and increased myocardial oxygen demand and should be used with caution in patients with cardiac comorbidities. Although not tested in this study, norepinephrine, a combined α−1) rather than volume per time unit (ml min−1), and is no longer reliable when the diameter of the vessel changes.Paco2 levels are known to influence cerebral blood flow, it is important to keep these constant, which was confirmed by stable ETCO2 levels throughout the conduct of the study. Third, owing to the crossover design of this study we were able to eliminate substantial between-patient variability. Finally, a wash-out period was used between both interventions and there was no carry-over effect.This study has several strengths. First, cerebral bypass procedures provided us the opportunity to measure cerebral perfusion invasively with an ultrasonographic flow meter in close proximity to the middle cerebral artery, providing reliable measurements.,,,,Nevertheless, this study has several obvious limitations. First, most patients suffered from moyamoya disease, which limits generalisability. However, the results from this study might also be applicable to a broader population at risk for perioperative cerebral ischaemia. Like in moyamoya disease, cardiac surgery patients, post-ischaemic stroke patients and patients after subarachnoid haemorrhages or traumatic brain injury all suffer from a disrupted autoregulation.Both administration of dobutamine, by increasing cardiac output while decreasing MAP, and phenylephrine, by increasing MAP while maintaining cardiac output, increased graft flow in patients undergoing cerebral bypass surgery.Study design: AA, JARvW, TPCD, EECdW, AvdZ, CJK, WAvKPatient recruitment: AAData collection: AAData analysis: AA, JARvWData interpretation: AA, JARvW, TPCD, GJER, AvdZ, CJK, WAvKDrafting of the manuscript: AA, JARvW, WAvKCritical revision of the manuscript: JARvW, TPCD, EECdW, GJER, AvdZ, CJK, WAvKFinal approval of the manuscript: all authors.The authors declare that they have no conflicts of interest.Departmental sources.


The objective of this study was to develop a new predictive equation of resting energy expenditure (REE) for acute kidney injury patients (AKI) on dialysis.A cross-sectional descriptive study was carried out of 114 AKI patients, consecutively selected, on dialysis and mechanical ventilation, aged between 19 and 95 years. For construction of the predictive model, 80% of cases were randomly separated to training and 20% of unused cases to validation. Several machine learning models were tested in the training data: linear regression with stepwise, rpart, support vector machine with radial kernel, generalised boosting machine and random forest. The models were selected by ten-fold cross-validation and the performances evaluated by the root mean square error.There were 364 indirect calorimetry measurements in 114 patients, mean age of 60.65 ± 16.9 years and 68.4% were males. The average REE was 2081 ± 645 kcal. REE was positively correlated with C-reactive protein, minute volume (MV), expiratory positive airway pressure, serum urea, body mass index and inversely with age. The principal variables included in the selected model were age, body mass index, use of vasopressors, expiratory positive airway pressure, MV, C-reactive protein, temperature and serum urea. The final r-value in the validation set was 0.69.We propose a new predictive equation for estimating the REE of AKI patients on dialysis that uses a non-linear approach with better performance than actual models. Acute kidney injury (AKI) occurs in approximately 3–15% of hospitalised patients and can affect 30–50% of patients admitted to intensive care units (ICU). It is associated with extremely high mortality rates, ranging from 20 to 50% , 5. AccuDetermining energy requirements in critically ill patients via indirect calorimetry (IC) has long been considered the gold standard . LimitatThe present study aimed, therefore, to develop a new predictive equation of REE for AKI patients, on dialysis and mechanically ventilated, using a machine learning approach.2, who gave their consent to participate in the study and agreed to comply with its standards were included. Inclusion criteria were patients admitted to the ICU with a diagnosis of AKI according to the KDIGO criteria [2) greater than 0.60, expiratory positive airway pressure (PEEP) > 10 cm H2O, maximum airway pressure > 60 cm H2O, stirring presence, use of neuromuscular blockers, air leakage into the ventilator circuit around the endotracheal tube cuff or from a bronchopleural fistula, as these factors lead to inaccuracies in REE measurement by IC. IC was performed using the RMR Quark apparatus .A cross-sectional descriptive study was carried out in the Dialysis Unit of the Clinical Hospital of Botucatu Medical School. The project was approved by the centre’s ethical committee and the protocols used followed the criteria of the Helsinki Declaration (protocol 4383/2012). For development of the predictive equation, 364 indirect calorimetry (IC) measurements in 114 AKI patients on dialysis were consecutively selected, aged between 19 and 95 years. Subjects with a body mass index (BMI) between 18–55 kg/mcriteria , clinicaFor construction of the predictive model, 80% of the cases were randomly separated. Twenty per cent of unused cases were separated for validation. The pre-processing was applied to the training and test set. The continuous predictor variables were transformed using Box and Cox and after dividing by means (centre) and standard deviation . This transformation led to a uniform scale  for all analyses so that they were comparable between analytical platforms. The missing variables were imputed by the median. Categorical variables were converted into dummy variables. In feature engineering, we tested the distribution of each continuous variable according to the outcome. We choose natural splines with three degrees of freedom for age and BMI aims to account for the non-linearity. Afterward, linear and non-linear models were tested in the training data. These models were: linear regression with stepwise selection, linear regression with regularisation (glmnet), rpart, support vector machine with radial kernel (SVM), generalised boosting machine (GBM), extreme gradient boosting (XGBoost) and random forest. The optimisation of hyperparameters was done in the training set with ten-fold cross-validation and the performance was evaluated by the root mean square error (RMSE). Therefore, we evaluated the final performance of the best model in the test set by choosing the model with the lower RMSE . Table A total of 364 IC measures in 114 patients were evaluated. The mean age was 60.65 ± 16.9 years and 68.4% were males. Diagnoses of sepsis and cardiovascular disease (CVD) accounted for 79.8% of hospitalisations. AKI was the aetiology associated with sepsis in most patients (81.6%). The REE estimated by the Harris–Benedict (HB) formula averaged 1540 ± 346 kcal; whereas the REE measured by the IC was significantly higher 2081 ± 645 kcal  are summarised in attachment 01. In the exploratory data analysis, we plotted the REE related to age and BMI Fig. . We demoWe trained different algorithms by ten-fold cross-validation in the training set (80% of the data) and the hyperparameters of each model were tuned based in the lower RMSE. After training tuned models, we applied resamples of the training data and selected the best model based on the lower RMSE Fig. . TherefoThe models that had a non-linear approach had a lower RMSE. The linear model may have had the advantage of simplicity and interpretability (attachment 02). The model with the best accuracy was selected by the lower RMSE: random forest. The variable importance of the final model (random forest) was plotted in Fig. Finally, we plotted a correlation between the predicted values of REE and observed REE in the test set (unseen data). The random forest model (best model) had a final r-value in this test set of 0.69 compared to an r-value of 0.24 with the Harris–Benedict equation Fig. .Fig. 5A Information about the energy expenditure assessment of patients with AKI is scarce in the literature. Our study found that the REE estimated by the Harris and Benedict formula  was signIt has long been held that critical illness is a hypermetabolic state, i.e. that the basal metabolic rate is higher than that predicted by simple population characteristics –27. PhysThis study aimed to develop and validate predictive equations for REE in severe AKI patients using a machine learning approach. It was found that the models were developed validly and significantly predicted REE in these patients and according to several linear and non-linear algorithms. In the present study, REE was positively correlated with C-reactive protein, minute volume (MV), expiratory positive airway pressure, serum urea, BMI and inversely with age (attachment 02). The principal variables included in the best model were age, BMI, use of vasopressors, expiratory positive airway pressure, minute volume, C-reactive protein, temperature and serum urea. The final r-value in the validation set was 0.69. In the literature, there is no consensus regarding the procedures to be used for the validation of predictive models. Some authors do not suggest the use of determination and correlation coefficients for the validation of techniques or estimated variables . Others 2 = 0.21, data not shown).Another advantage of the machine learning approach is to demonstrate other predictor variables that influence the outcome by finding complex interactions. The principal factors that contributed to the variability of REE were the BMI and age that were previously described with traditional models , 25. TheThus, this study demonstrates the importance of estimating the REE in severe AKI patients, which is a determining factor in the assessment of nutrition status in critically ill patients. Previous studies reported correlations between morbidity or mortality and REE and indicated that sicker patients and non-survivors had a lower REE , 25 but From a practical and application point of view, it is worth mentioning the capacity for technical evaluations to reduce the difference in REE between IC and that estimated by other conventional formulas, such as HB, the use of these predictive equations for the assessment of passive and active trawling, contributing with relevant information for nutritionists and physicians.The present study has as main limitations the fact that the predictive models are not valid for non- severe AKI patients and the Bland Altman model was not performed. Although we trained models with a robust estimator with ten-fold cross-validation using RMSE as a metric, an approach regularly used in machine learning analysis, we tested the model in unseen data that may be considered an internal validation.We propose a new predictive equation for estimating the REE of AKI patients on dialysis that uses a non-linear approach with better performance than actual models.


Patients with chronic cardiorenal syndrome type 2 (T2-CRS) who qualify for resynchronization therapy (CRT) are exposed perioperatively to potentially nephrotoxic factors including contrast agents and blood loss. CKD-EPI]), and the electrolyte and urine biomarkers albumin (uAlb), albumin/creatinine ratio (UACR), and neutrophil gelatinase-associated lipocalin (uNGAL).  The objective of this prospective interventional study was to assess the effects of CRT on renal function in patients with T2-CRS within the first 48 hours following implantation. Initially, 76 patients  with heart failure (New York Heart Association classes II–IV), ejection fraction ≤ 35%, and QRS > 130 ms were included in the study. During CRT implantation, a nonionic contrast agent (72.2 ± 44.9 mL) was administered. Prior to and 48 hours following implantation, renal function was evaluated using the following serum biomarkers: creatinine (sCr), estimated glomerular filtration rate (using the Chronic Kidney Disease Epidemiology Collaboration equation [eGFRp=0.041). After CRT implantation, uAlb, UACR, and potassium levels were reduced (p < 0.05), and uNGAL, sCr, and eGFRCKD-EPI were unchanged. The contrast medium volume did not correlate with the test biomarkers (p > 0.05).  Before CRT, patients classified as NYHA class III or IV had higher uNGAL levels in comparison to uNGAL levels after CRT (43.63 ± 60.02 versus 16.63 ± 18.19;  In patients with T2-CRS, uNGAL is a biomarker of kidney injury that correlates with the NYHA classes. A stable uNGAL value before and after CRT implantation confirms the lack of risk of contrast-induced nephropathy. Reduced albuminuria and blood potassium are biomarkers of improving T2-CRS in the early post-CRT period. Chronic kidney disease (CKD), abnormalities in kidney structure or function present for >3 months with health implications , affectsThe objective of this study was to assess the impact of CRT implantation on renal function as indicated by classic and modern biomarkers in patients with chronic cardiorenal syndrome type 2 (T2-CRS) within the first 48 hours following surgery.From September 1, 2016, until December 31, 2018, 76 patients with heart failure, who had met the criteria for CRT implantation according to the European Society of Cardiology guidelines for the treatment of heart failure, were prequalified for the prospective interventional study . The algn = 67; 88.16%), a procedure that included the option of defibrillator therapy (CRT-D) was used. CRT-D implantation was performed de novo in 50 (65.8%) people. In the CRT-D group, implantation of the device was primarily performed to prevent the risk of sudden death  and to convert previous ventricular tachycardia/ventricular fibrillation arrhythmias . In the remaining 17 (22.37%) patients, the previously implanted systems were upgraded. According to the recommendations of the European Society of Cardiology [Nine (11.84%) patients had a resynchronization stimulator (CRT-P) implanted, and in all the other cases , posterior-posterior , and middle  cardiac veins. Twelve patients (15.79%) were excluded from further analysis for the following reasons: presence of conditions causing an increase in uNGAL irrespective of kidney function , failure to provide written consent or withdrawal of the patient's consent to participate in the study (n = 4), and left-cell electrode dislocation (n = 2).The left ventricular electrodes were located in the lateral .To determine the effectiveness of CRT at 48 hours after implantation, the QRS width and NYHA heart rate class were assessed .CKD-EPI), albuminuria in a single morning urine sample (uAlb), and the creatinine albumin ratio in a single morning urine sample (uACR). Additionally, the following modern biomarkers were assessed: lipocalin associated with neutrophil gelatinase in a single morning urine sample (uNGAL) and the uNGAL to creatinine ratio (uNCR) in a single morning urine sample.To assess renal function, the following classic biomarkers were used: serum creatinine (sCr), estimated glomerular filtration using the CKD-EPI equation  and a chemiluminescent microparticle immunoassay.Selected demographic and clinical data for participants and laboratory results prior to CRT implantation are presented in AKI was diagnosed according to the Kidney Disease Improving Global Outcomes (KDIGO) guidelines, that is, when serum creatinine increased to ≥26.5 mmol/L within 48 hours or ≥1.5 times, either verified or presumed to occur within 7 days .2: 2 points if 60–40, 4 points if 20–40, and 6 points if <20. Hypotonia requiring the use of pressure amines was not observed in the study group. No patient had an intra-aortic balloon pump.During CRT implantation, a nonionic contrast agent  was administered. To assess the risk of contrast-induced nephropathy in the study population, the Mehran model  was usedAll patients gave their written informed consent to participate in the study. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Bioethics Committee of the University of Rzeszów (Reference No. 5/04/2016) N.t-test for two groups, and one-way analysis of variance (ANOVA) with additional post hoc comparisons was used for three or more groups. Continuous variables with nonnormal distribution were compared using the Mann–Whitney U test for two groups or the Kruskal–Wallis ANOVA with additional post hoc comparisons for three or more groups. The Bonferroni correction for multiple testing was applied. Spearman's rank coefficient was calculated to assess correlations. The Statistica 12.5 PL package  was used for other analyses. A pvalue < 0.05 was considered statistically significant.Categorical variables are presented as numbers with percentages in brackets. Differences between categorical variables were evaluated using Pearson's chi-square test and Fisher's two-tailed exact test. Continuous variables are presented as medians with interquartile ranges in brackets. The Shapiro–Wilk test was used for the distribution assessment of continuous variables. Continuous variables with normal distribution were compared using Student's Selected clinical and demographic data characterizing the study group prior to CRT implantation are described in n = 27) and NYHA III and IV combined (n = 37). For further statistical analysis, the NYHA classes III and IV were combined into one group, because in some patients it was difficult to clearly specify which of the two classes they belonged to, and because of the low number of subjects with evident heart failure among the NYHA class IV outpatients. Selected demographic and clinical data and test results in the study group prior to CRT implantation according to NYHA heart failure classes are described in To assess the initial renal function depending on the NYHA class, the research group was divided into two groups: NYHA II (Significant correlations of uNGAL with NYHA heart failure classes in the study group prior to CRT implantation are presented in p > 0.05) and patients with hypertension (p > 0.05).The effect of concomitant T2DM or hypertension on renal function prior to CRT implantation was analyzed. No difference was observed in either group of patients prior to implantation: patients with T2DM  and uACR  48 hours following CRT implantation compared with pre-CRT values, respectively. However, in the group of patients with concomitant HA, a significant decrease in eGFR was observed  as well as a decrease in urinary albumin excretion  48 hours following CRT implantation compared with pre-CRT values, respectively.The impact of coexisting T2DM and HA on CRT implantation was also analyzed. It was observed that patients with T2DM had significantly lower albumin excretion  [p > 0.05).During CRT implantation, a nonionic contrast agent  with an average dose of 72.2 ± 44.9 ml  was administered. Using the Mehran risk score (MRS) , the ris > 0.05) . Using tAmong the analyzed patients in whom CRT was implanted, an increase in creatinine above 0.3 mL/min from baseline was observed in two (3.125%) patients. According to the KDIGO 2012 definition , these pSignificant comorbidities are common in patients with severe heart failure, as we have experienced in clinical practice. One of the conditions often associated with CHF is CKD. CKD resulting from CHF is known as T2-CRS , 19. PatTherefore, we decided to assess the kidney function of patients with severe heart failure who qualified for CRT. Resynchronization is a method of cardiac pacing that aims to restore the optimal sequence of myocardial contraction and consequently improves both its systolic and diastolic functions, reduces functional mitral valve regurgitation, reverses adverse left ventricular remodeling, and reduces NYHA clinical symptoms .Patients who met the T2-CRS criteria qualified for surgery in our study. In the study population, most patients met the classic CKD G1–G3/A1-A2 criteria according to KDIGO [We observed a significantly larger increase in NGAL in a single sample of urine prior to CRT implantation in patients with heart failure in NYHA classes III and IV than in patients in NYHA class II. NGAL is a small protein with a molecular weight of 25-kDa, freely filtered through the glomeruli, and then reabsorbed into the blood in the proximal tubule . The larIt should be remembered that the assessment of uNGAL as an indicator of renal function can only be objective after excluding factors that may affect its concentration regardless of renal function , 26. Hen2 [Implantation patients are exposed to a contrast medium during the procedure. This factor may contribute to the possibility of deterioration of renal function in the perioperative period due to the prerenal mechanism (blood loss) as well as the renal-derived mechanism (contrast-induced nephropathy). In our study, at 48 hours following successful CRT implantation, we observed that all patients with T2-CRS, despite the use of a potentially nephrotoxic contrast agent and anemization, revealed a significant improvement in albuminuria and no increase in uNGAL. Hence, we believe that the assessment of albuminuria at 48 hours following CRT implantation provides a relatively immediate indicator of the effectiveness of implantation in terms of improved renal function in patients with T2-CRS. In addition, in our study, the largest decrease in albuminuria was observed in the group of patients with higher NYHA classes and coexisting T2DM. A decrease in albuminuria is a classic and important prognostic factor for improved renal function, reduced total mortality, reduced mortality due to cardiovascular reasons, and a reduction in the number of repeated hospitalizations in patients with heart failure –28. It w2 . Our obs2 , which t2 . An inte2 , 19. Mea2 . In the 2 .Despite some interesting observations and results, in our opinion, the current research work has its limitations. The limitations of the study are the small size of the research group and the lack of a control group.uNGAL is a biomarker of kidney function that correlates with NYHA classes in T2-CRS patients. CRT implantation in patients with T2-CRS is not a risk factor for postcontrast nephropathy as evidenced by the lack of uNGAL and uNCR increases and stable glomerular filtration in patients 48 hours following CRT implantation.Assessment of albuminuria 48 hours following CRT implantation can be considered an early and noninvasive biomarker of implantation efficiency in terms of improved renal function in patients with T2-CRS. This finding is clinically relevant considering the proven association of increased albuminuria with the risk of cardiovascular complications and the progression of CKD. A significant decrease in potassium levels 48 hours following the implantation of CRT is a biomarker of subclinical improvement in renal function in patients with T2-CRS, regardless of the severity of any clinical symptoms of heart failure or comorbidities. –33