Background. Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) is the etiological agent of the coronavirus disease 2019 (COVID-19) pandemic. Among the risk factors associated with the severity of this disease is the presence of several metabolic disorders.For this reason, the aim of this research wasto identify the comorbidities and laboratory parameters among COVID-19 patients admitted to the intensive care unit (ICU), comparing the patients who required invasive mechanical ventilation (IMV) with those who did not require IMV, in order to determine the clinical characteristics associated with the COVID-19 severity.Methods. We carried out a cross-sectional study among 152 patients who were admitted to the ICU from April 1st to July 31st, 2021, in whom the comorbidities and laboratory parameters associated with the SARS-CoV-2 infection severity were identified. The data of these patients was grouped into two main groups: “patients who required IMV” and “patients who did not require IMV”. The nonparametric Mann–Whitney U test for continuous data and theχ2 test for categorical data were used to compare the variables between both groups.Results. Of the152 COVID-19 patients who were admitted to the ICU, 66 required IMV and 86 did not require IMV. Regarding the comorbidities found in these patients, a higher prevalence of type 2 diabetes mellitus (T2DM), hypertension and obesity was observed among patients who required IMV vs. those who did not require IMV (p<0.05). Concerning laboratory parameters, only glucose, Interleukin 6 (IL-6), lactate dehydrogenase (LDH) and C-reactive protein (CRP) were significantly higher among patients who required IMV than in those who did not require IMV (p<0.05).Conclusion. This study performed in a Mexican population indicates that comorbidities such as: T2DM, hypertension and obesity, as well as elevated levels of glucose, IL-6, LDH and CRP are associated with the COVID-19 severity. ACE-2: angiotensin-converting enzyme 2ALT: alanine transaminaseAST: aspartate transaminaseCOPD: chronic obstructive pulmonary diseaseCOVID-19: coronavirus disease-2019CKD: chronic kidney diseaseCRP: C-reactive proteinEWS: Early Warning Score2: fraction of inspired oxygenFiOICU: intensive care unitIL-1β: Interleukin 1 betaIL-6: interleukin 6IMV: invasive mechanical ventilationMSQ: Medical Symptom Questionnaire2: partial pressure of oxygenPaOPCR: polymerase chain reactionRAAS: Renin-Angiotensin-Aldosterone SystemSARS-CoV-2: severe acute respiratory syndrome-coronavirus 2SCQ: Self-administered Comorbidity Questionnaire2: blood oxygen saturationspOT2DM: type 2 diabetes mellitustumor necrosis factor alphaTNFα:WHO: World Health Organization.– The clinical manifestations of the disease caused by SARS-CoV-2, known as coronavirus disease 2019 (COVID-19), are highly variable and range from asymptomatic forms, moderate manifestations and even severe complications, such as: pneumonia, respiratory failure, septic shock, multiple organ dysfunction and death., Unfortunately, the molecular mechanisms involved with COVID-19 severity seem to be particularly complex, due to important immunopathological changes induced by SARS-CoV-2;– as well as metabolic conditions  that underlie the clinical presentation in these patients.– Regarding latter, a growing body of evidence has suggested that these comorbidities contribute significantly to increased COVID-19 severity and fatal outcomes.,– For instance, several studies have reported that obesity is a comorbidity that increases the risk of complications in SARS-CoV-2 infection, for the following reasons:(a) it has been suggested that the ACE-2 receptor expression (target of SARS-CoV-2) is higher in adipose tissue than in the lung parenchyma, which makes adipose tissue an important viral reservoir  the Renin-Angiotensin-Aldosterone System (RAAS), a hormonal cascade which regulates blood pressure and is generally overactive in obese patients, has been linked to SARS-CoV-2 cellular infection as well as myocardial and lung injury;, and(c) it is well known that obesity is related to an increase in circulating levels of many adipokines and pro-inflammatory mediators released by adipocytes. Therefore, obesity-induced adipose tissue inflammation generates important metabolic abnormalities and disproportionate effects on the immune system, which are relevant pathophysiological aspects in COVID-19 severity.,, On the other hand, in comorbidities like hypertension and type 2 diabetes mellitus (T2DM), besides the previously mentioned points, severe metabolic dysfunctions and several coagulation system alterations take place.– For example:(a) it has been reported that the endothelial dysfunction in obese patients with hypertension promotes the development of a hypercoagulable pro-thrombotic state (by exposure of tissue factor and other pathways), which contributes markedly to life-threatening complications of COVID-19, such as venous thromboembolic disease, systemic vasculitis, endothelial cell apoptosis and multiple organ involvement;, and(b) it is also clear that insulin resistance contributes substantially to the more severe phenotype associated with obesity and T2DM in COVID-19., In fact, it has been suggested that the SARS-CoV-2 infection could cause disturbances in glucose metabolism, therefore the acute hyperinflammatory state itself could worsen insulin resistance in these patients., In this context, these important pathophysiological alterations in COVID-19 patients have led to an urgent necessity in identifying clinical laboratory predictors, which could provide relevant information in determination of prognosis, patient follow-up, and therapeutic monitoring, as well as differentiate severe patients from the mild/moderate form of COVID-19., For instance, biomarkers of an overactive innate immune system, such as markedly elevated neutrophil count, IL-6, C-reactive protein and serum ferritin, could help recognize a potential severe SARS-CoV-2 infection during triage, while biomarkers of organ failure could be helpful in monitoring evolution of COVID-19 patients admitted to the intensive care unit (ICU)., Thus, both the etiological diagnosis of SARS-CoV-2 and the classification of these patients are the most obvious scenarios in the current health crisis, in which the clinical laboratory plays a fundamental role.,, Because of all the above described, the early identification of comorbid conditions and laboratory predictors associated with the SARS-CoV-2 infection severity, as well as the rapid application of measures to control this infection are currently the main strategies to prevent and reduce the risk of the virus spreading. For this reason, the present study aimed to identify the comorbidities and laboratory parameters among COVID-19 patients admitted to the ICU, comparing those patients who required invasive mechanical ventilation (IMV) to those who did not require IMV, in order to determine the clinical characteristics associated with the COVID-19 severity.Without a doubt, the current pandemic caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) represents one of the greatest public health challenges, which has led to extensive worldwide research efforts to identify individuals at greatest risk of developing critical illness.voir see;19,What are the comorbidities and laboratory parameters associated with SARS-CoV-2 infection severity in patients from the southeast of Mexico?This research used a cross-sectional design.st to July 31st, 2021. For the confirmatory diagnosis of this disease, nasopharyngeal and throat swab specimens were collected upon admission, which were subsequently analyzed by real-time polymerase chain reaction (qPCR) for SARS-CoV-2 RNA detection. Case definitions for these patients were in accordance with the interim guidance of the World Health Organization (WHO), which includes: fever, cough, dyspnea, respiratory frequency ≥ 30/min, SpO2 ≤93%, PaO2/FiO2 ratio <300 and lung infiltrates >50%, as severe clinical manifestations of COVID-19. It is necessary to highlight that this is one of the most important hospitals in the southeast of Mexico and has been designated by the Federal Secretary of Health for the hospitalization of COVID-19 patients since February 2020. Identification of these patients was achieved by reviewing and analyzing admission records and clinical histories from all available electronic medical records. Therefore, patients with clinical data of pneumonia, but with negative SARS-CoV-2 test results were excluded from this study. Additionally, since it has been documented that both phenotypic and genotypic characteristics could contribute substantially to the development of specific comorbidities,– COVID-19 patients from other ethnicities  were also excluded from the present study.The present study enrolled 152 patients diagnosed with COVID-19, who were admitted to the ICU of the General Hospital “Dr. Desiderio G. Rosado Carbajal" from April 1 and all procedures involving research study participants were requested and verbally approved by the Ethics Commission of the hospital on March 16th, 2021. Written informed consent was waived by the Ethics Commission of the designated hospital due to the rapid onset of this public health emergency. Verbal consent of the patients was witnessed by a medical professional assigned to the hospital (D.C.E.) and formally documented in the medical record. However, regarding patients who were unable to provide this consent due to their severe clinical condition, a patient's family member provided it, which was then confirmed once the patient was found lucid.This cross-sectional study was conducted according to the guidelines laid down in the Declaration of Helsinki an instrument that asks about the presence, treatment and functional limitations of 12 common comorbidities and three additional non-specified medical problems. 3) Clinical symptoms, which included: fever, cough, sore throat, nasal congestion, breathing difficulties, headache, myalgia, diarrhea, vomiting and nausea. These were measured according to the medical symptom questionnaire (MSQ) that identifies several symptoms which help to find the underlying causes of illness, by using 15 categories in which the patients rate a particular symptom from 0 (never experienced) to 4 (frequently experienced and severe) (available fromLake Travis Integrative Medicine). Here it is important to note that in those cases where the patient was unable to provide the information described above  due to their severe clinical condition (including confused and unconscious states), a patient's family member provided it. 4) Vital signs, such as: temperature, spO2, respiratory and heart rate, which were measured using a monitoring equipment and chosen according to the early warning score (EWS), a physiological scoring system based on the individual values of multiple vital signs to quickly evaluate the level of clinical deterioration, in both emergency and general care conditions. Approximately 15 minutes after admission, the blood samples were collected and sent to the laboratory to be analyzed according to standard methods using equipment like AcT5diff hematology analyzer (Beckman Coulter), Synchron LX-20 chemistry analyzer (Beckman Coulter) and Lumipulse G600II analyzer (Fujirebio). Briefly, the laboratory tests and how they were measured are mentioned below: complete blood count (CBC) determined by electrical impedance; blood chemistry and liver function test, whose parameters were measured by colorimetry methods; serum electrolytes determined by potentiometry and electrical conductivity method; C-reactive protein (CRP), interleukin 6 (IL-6) and lactate dehydrogenase (LDH) as inflammation-related biomarkers, which were measured by turbidimetric, chemiluminescent and kinetic UV method, respectively. Thus, these laboratory tests were part of the standard of medical care. Finally, all the data mentioned in this section was recorded in an electronic database by two independent researchers (D.C.E. and J.G.C.) and verified by two experienced doctors (G.G.J.A. and L.V.C.J.).Concerning this point, the data collected at the time of admission was the following. 1) Demographic data, including age and gender. 2) Comorbidities, such as T2DM, hypertension, dyslipidemia, chronic kidney disease, heart disease, chronic obstructive pulmonary disease, obesity and malignancy, which were chosen and determined according to the self-administered comorbidity questionnaire (SCQ),2χ test for categorical data were used to compare variables between both groups. On the other hand, in order to evaluate the laboratory parameters in predicting the COVID-19 severity, the Receiver Operating Characteristic (ROC) curves were plotted corresponding to the variables found to show significance, with the corresponding areas under the curve (AUC), sensitivity, specificity, 95% confidence intervals (95%CI), as well as the optimal cutoff, which was defined as the value maximizing the Youden index. Ap value <0.05 was considered statistically significant. All statistical analyses were performed using SPSS Statistics version 23.0 software, and figures created with SPSS and CorelDRAW graphics suite 2020, or Inkscape 0.92 could also be used as an alternative. Lastly, it is important to mention that a large number of patients who were included in this research (n=127) had incomplete data in their medical records concerning socioeconomic status, sanitary conditions, physical activity, nutritional habits, household income and access to healthcare services, so it was decided not to capture this information in the database, in order to reduce biases in the interpretation of the results.With the purpose of understanding the comorbidities and laboratory parameters associated with the SARS-CoV-2 infection severity in patients from the Mexican southeast, the data collected was grouped into two main groups: patients who required invasive mechanical ventilation (IMV) and patients who did not require IMV. The continuous data was described as mean and standard error, while categorical data was described as percentages. The nonparametric Mann–Whitney U test for continuous data andp<0.05). In contrast, here it should be emphasized that while dyslipidemia was a prevalent condition among the COVID-19 patients admitted to the ICU (n=67), this did not show significant differences when both patient groups were compared. Finally, the least prevalent comorbidities in the whole sample were as follows: chronic kidney disease (n=8), heart disease (n=5), chronic obstructive pulmonary disease (n=12) and malignancy (n=1), in which no significant differences were found either.As shown inRegarding the clinical symptoms, we observed that the COVID-19 patients admitted to the ICU showed common symptoms of acute respiratory infection see, such asAs observed inWe performed ROC curves on the above laboratory parameters with significant differences to assess their value predictive in the COVID-19 severity . For ins, which could partially explain the high hospital mortality rate related to COVID-19 in this country; in fact, according to data compiled byJohn Hopkins University, until October 2021, more than 284,381 deaths had been registered in Mexico. In this context, several studies support our findings, in which it has been documented that COVID-19 patients suffering from these metabolic disorders increase the need for critical care and particularly for IMV requirement.– For instance, Simonnetet al. and Costa Monteiroet al. reported an elevated prevalence of obesity and T2DM among ICU-admitted patients infected with SARS-CoV-2. These studies indicated that these comorbidities could serve as clinical predictors for risk stratification models. Likewise, these studies concluded that early measurement of anthropometric and metabolic parameters in these patients could be crucial to avoid unfavorable clinical outcomes., Similarly, Busettoet al., Cummingset al. and Borobiaet al. evaluated the clinical course of critically ill patients with COVID-19. In summary, these studies reported that COVID-19 patients with obesity, T2DM and hypertension showed a higher risk of more severe clinical symptoms and extrapulmonary organ dysfunction during SARS-CoV-2 infection, thus requiring a more frequent need for ICU admission and IMV., Likewise, Petrilliet al. conducted a study including more than 4000 COVID-19 cases, in which obesity was the strongest predictor of critical illness, substantially higher than pulmonary or cardiovascular diseases. In this regard, precise pathophysiological mechanisms related to a higher prevalence of these comorbidities among COVID-19 patients requiring IMV are not completely understood. However, recently several studies have reported that these metabolic disorders are multifactorial conditions that are closely associated with severe respiratory dysfunctions as well as impaired molecular mechanisms that could worsen the course of SARS-CoV-2 infection., For example,(a) it is clear that obesity causes mechanical compression of the diaphragm, thoracic cavity and lungs, which could lead to a restrictive pulmonary damage and consequently to an impaired respiratory ventilation., Moreover, several studies have indicated that an excessive adipose tissue amount in the abdominal area reduces the strength of the respiratory muscles, decreases the total compliance of the respiratory system and increases pulmonary resistance.–(b) It has been reported that obese patients with hypertension are more likely to develop severe respiratory diseases, such as: asthma, chronic obstructive pulmonary disease,, obesity hypoventilation syndrome,, pulmonary hypertension and obstructive sleep apnea,, which predisposes them to low levels of blood oxygenation and evidently to fatal respiratory outcomes in severe SARS-CoV-2 infection.,(c) T2DM could negatively impact clinical outcomes in COVID-19 patients admitted to the ICU, since it has been documented that hyperglycemia in diabetic patients could increase SARS-CoV-2 replication, at the same time aerobic glycolysis could facilitate SARS-CoV-2 replication via synthesis of mitochondrial reactive oxygen species and activation of hypoxia-inducible factor 1α., Thus, alterations in glucose metabolism could also have influenced a greater need for IMV as well in the poor prognosis in these patients., On the other hand, several hypotheses have emerged suggesting that SARS-CoV-2 could also be a key contributor in the worsening of metabolic status in comorbid patients requiring IMV, for example:(a) acute inflammatory state induced by SARS-COV-2 could alter the lipid and glucose metabolism. This hypothesis is supported by the fact that pro-inflammatory cytokines  modulate the metabolism of these biomolecules; hence, dyslipidemia and hyperglycemia observed in the ICU-admitted patients could also be due to an inadequate cellular secretion of cytokines and/or an inappropriate immune response induced by SARS-CoV-2.,(b) Oxidative stress promoted by SARS-COV-2 infection could exacerbate dyslipidemia in COVID-19 patients with underlying metabolic disorders. This argument arises from the fact that most viral infections manipulate antioxidant systems in several chronic conditions, leading to abnormalities in cellular metabolism.–(c) SARS-CoV-2 could directly affect liver function and thus alter the lipid biosynthesis. This hypothesis could partially explain the biochemical changes found in our study, in which a slight increase in serum aspartate transaminase (AST) and alanine transaminase (ALT) levels was observed. However, these slight changes likely do not contribute significantly to the increased levels of cholesterol and triglycerides in the COVID-19 patients admitted to the ICU. Finally, in our study the elevated levels of CRP, IL-6 and LDH were the most specific and statistically significant parameters in both groups of patients, which suggests that these molecules could play a key role during the progression and the prognosis of fatal outcomes in COVID-19 patients who required IMV.,, In fact, these biomarkers have previously been associated with the severity and mortality of COVID-19 in most cases defined by the Chinese National Health Commission.– Moreover, recent publications have provided additional information that strengthen the role of CRP, IL-6 and LDH as predictive markers of SARS-CoV-2 infection severity, especially in critically ill patients.,,In this paper, the comorbidities and laboratory parameters associated with SARS-CoV-2 infection severity in patients from the Mexican southeast were analyzed. Regarding the comorbidities found in our study, a higher prevalence of obesity, T2DM and hypertension in those patients who required IMV was observed (p<0.05). These findings attract a lot of attention, since unfortunately Mexico is among the highest places in terms of prevalence of these comorbidities,A strength of this research is that it provides scientific evidence indicating that comorbidities such as obesity, T2DM, and hypertension, as well as elevated levels of glucose, IL-6, LDH and CRP are associated with the COVID-19 severity among ICU-admitted patients. Moreover, the clinical and laboratory data was collected from one of the most important hospitals in the Mexican southeast, which concentrates a large part of COVID-19 patients in that region. Finally, our study has some limitations inherent to methodological design that could affect the interpretation of results. First, since our study included only COVID-19 patients from the southeast of Mexico, one needs to be careful to extrapolate our findings to those who reside in other geographical areas of the country and the world. Second, only the basal clinical findings were included in this research, while the clinical changes induced by disease progression, pharmacological treatment and invasive mechanical ventilation were not addressed in this paper, which could lead to important biases of clinical observation and interpretation in these patients. Third, the small number of patients included in this study limit the ability to determine causal inferences linked to the COVID-19 severity. Therefore, randomized clinical trials and observational studies (with a larger number of patients) addressing the factors underlying the severe conditions of COVID-19 in patients with obesity, hypertension, T2DM and/or metabolic dysfunction could contribute to determine the causes associated with the clinical progression and severity of this disease. Fourth, the retrospective design of the present study is also an important limitation, since a large number of cases included in this study had incomplete data in their medical records ; thus, it was not possible to adjust the risks associated with the COVID-19 severity in these patients. Besides, it is likely that the categorical stratification used in our study was not the most appropriate method, since a validated severity scale for COVID-19 patients admitted to ICU was not used; hence, the results presented in this research should be viewed with caution.In conclusion, the results of this retrospective case study performed in a Mexican population indicates that metabolic disorders such as: obesity, T2DM, and hypertension, as well as elevated levels glucose, IL-6, LDH and CRP are associated with the SARS-CoV-2 infection severity. Therefore, patients suffering from these conditions should take additional measures to avoid COVID-19 infection by enforcing prevention during the current pandemic. Likewise, public health policies and social support services should focus on disadvantaged communities with high rates of obesity, T2DM, hypertension and nutritional disorders to promote healthy lifestyle choices and preventive strategies that help minimize the risks and health consequences of these diseases, including COVID-19 complications. Moreover, as further waves of the pandemic and new variants of faster spread than early forms of SARS-CoV-2 are expected, improvement of guidelines for individuals with these comorbidities is strongly recommended. Finally, our characterization provides a quick clinical guidance to stratify high susceptibility patients in SARS- CoV-2 infections.https://doi.org/10.7910/DVN/DFALL6Harvard Dataverse: Comorbidities and laboratory parameters associated with SARS-CoV-2 infection severity in patients from the southeast of Mexico: A cross-sectional study.-COVID19_Database (v1).tab .-Data key.docx (Data key for variables and abbreviations in the tab file)This project contains the following files:Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).Data are available under the terms of the I enjoyed reading this article evaluating comorbidities and laboratory parameters associated with Severe COVID-19 disease. This study re-enforces many findings that are already well known in COVID-19. The study enrolled 152 COVID-19 patients admitted to ICU. RT-PCR was used to confirm the diagnosis of COVID. Patients requiring Invasive Mechanical Ventilation were classified as Severe COVID-19 cases. The comparison was made between patients who required invasive mechanical ventilation with those who did not. Limitations of the study: A small sample size of 152 patients. Many studies looking at the same question have been done on much larger populations. I agree with Invasive Mechanical Ventilation as the criteria for severity--however, there is some subjectivity in deciding who will be intubated. Therefore, this is a second but less critical limitation. The question is not novel--many findings are well known to us. It is well known that patients with obesity, type-2 diabetes, and those with higher CRP and IL-6 have more severe diseases. Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:COVID-19, Sepsis, Cardiomyopathy.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Reviewer 1 wrote:“No details of the analysis procedures of blood laboratory parameters were included, making the study replicable. Authors may add the detailed methodology at least for glucose, Interleukin 6, lactate dehydrogenase, and C-reactive protein or add the general method of the overall analysis.”Reply:Thank you very much for this important observation. Consequently, in this version of the manuscript the analyzers and methods used to determine the laboratory parameters have been mentioned at the end of the section of clinical data and sample collection.“The blood parameters were detected on the blood samples collected after 15min of admission of the patients, which makes it difficult to study to connect with the severity of COVID-19. If there is an opportunity to add the follow-up blood sample parameters like after 15 days of admission, the comparison with severity will be recognizable.”Reply:We appreciate your valuable comment. However, due to the nature of this research , a follow-up study of these patients was not considered (see second limitation at the end of the discussion section).  3. “Some grammatical errors were found  that should be corrected.”Reply:Thank you very much for your kind observation. Consequently, these grammatical errors have been corrected.  et al. evaluated the connection of comorbidities and laboratory parameters with the severity of SARS-CoV-2 infection. The authors recruited 152 ICU admitted COVID-19 patients, and among them, 66 required invasive mechanical ventilation (IMV), whereas 86 did not require IMV. Among the comorbidities, the authors found a connection of diabetes mellitus (T2DM), hypertension, and obesity with COVID-19 severity, whereas, among the laboratory parameters, glucose, Interleukin 6 (IL-6), lactate dehydrogenase (LDH), and C-reactive protein (CRP) were associated with the severity of COVID-19 considering the requirement of IMV. The manuscript has substantial merit for indexing. However, some points should be clarified from the authors' side.The manuscript entitled “Comorbidities and laboratory parameters associated with SARS-CoV-2 infection severity in patients from the southeast of Mexico: a cross-sectional study” submitted by Cruz-CanoNo details of the analysis procedures of blood laboratory parameters were included, making the study replicable. Authors may add the detailed methodology at least for glucose, Interleukin 6, lactate dehydrogenase, and C-reactive protein or add the general method of the overall analysis.The blood parameters were detected on the blood samples collected after 15min of admission of the patients, which makes it difficult to study to connect with the severity of COVID-19. If there is an opportunity to add the follow-up blood sample parameters like after 15 days of admission, the comparison with severity will be recognizable.Some grammatical errors were found  that should be corrected.  Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?PartlyReviewer Expertise:Pharmacology, Clinical Pharmacy, Pharmacogenomics, cancer genomicsI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Reviewer 2 wrote:“A small sample size of 152 patients. Many studies looking at the same question have been done on much larger populations.”Reply:Thank you very much for your kind comment. For this reason, the small sample size has been included in our study as an important limitation (see third limitation at the end of the discussion section).“I agree with Invasive Mechanical Ventilation as the criteria for severity--however, there is some subjectivity in deciding who will be intubated. Therefore, this is a second but less critical limitation.”Reply:We deeply appreciate your kind comment. Although it is true that in our paper we did not use a validated severity scale (as specified in the fifth and last limitation at the end of the discussion section), the criteria for invasive mechanical ventilation were based on clinical characteristics and laboratory parameters of these patients , which have also been used as severity predictors in similar studies, and recently included in validated scales as severity criteria for COVID-19.  Reviewer 2 wrote: “The question is not novel--many findings are well known to us. It is well known that patients with obesity, type-2 diabetes, and those with higher CRP and IL-6 have more severe diseases.”Reply: Thank you very much for your kind comment.Although this study shows many findings that are well known to the scientific community, we believe that this small research could modestly contribute to the reinforcement of the current evidence.


The assessment of movement reaction time (RT) as a sideline assessment is a valuable biomarker for mild TBI or concussion. However, such assessments require controlled laboratory environments, which may not be feasible for sideline testing during a game. Body-worn wearable devices are advantageous as being cost-effective, easy to don and use, wirelessly transmit data, and ensure unhindered movement performance. This study aimed to develop a Drop-stick Test System (DTS) with a wireless inertial sensor and confirm its reliability for different standing conditions (Foam versus No Foam) and task types , and postures (Standing versus sitting). Fourteen healthy young participants  participated in this study. The participants were asked to catch a falling stick attached to the sensor during a drop test. Reaction Times (RTs) were calculated from data for each trial from DTS and laboratory camera system (gold standard). Intraclass correlation coefficients  were computed to determine inter-instrument reliability. The RT measurements from participants using the camera system and sensor-based DTS showed moderate to good inter-instrument reliability with an overall ICC of 0.82 (95% CI 0.78–0.85). Bland–Altman plots and 95% levels of agreement revealed a bias where the DTS underestimated RT by approximately 50 ms. Assessment of reaction times can provide valuable insights into mild traumatic brain injury (mTBI), often referred to as a concussion. Most reaction time studies use optoelectronic motion capture systems in laboratory environments . AlthougResponse or Reaction Time (RT) is defined as the time following the presentation of a stimulus during which perceptual and motor planning computations take place before the movement, and increased RT is commonly associated with neurological disorders ,3,4,5,6.Decreasing the number and intensity of head impacts is essential to limiting the incidence of concussions. Several studies have found that athletes with faster RT sustained fewer and less severe impacts on the head ,16,17,18Presently, objective dynamic visual-motor physical response options for on-field concussion testing are limited, reducing the return to play decision confidence due to a reliance on data with little objectivity. Commonly, laboratory-based visual reaction times are evaluated by recording the time it takes following the presentation of a light stimulus for a button to be pressed . DespiteNew wearable technologies such as inertial sensors and smartphones have provided many integrated ways to quantify human movement in out-of-lab environments ,21. ThesEckner et al. (2009) proposed a more clinical RT test in which a participant was instructed to catch a weighted stick which was dropped in random intervals of 4–15 s . RT timeEckner and coworkers assessed the stick drop test using an accelerometer during a dual-task activity in which the athletes caught the stick only if a green LCD light was illuminated . HoweverSince RT appears to be a valuable identification metric to assess individuals who may have sustained a concussive event, it is clinically significant to have an inexpensive, reliable, and objective RT test translatable to the training room or on the sidelines. The development of this study’s DTS protocol using inertial sensors during single and dual tasks within multiple conditions is the first step toward providing objective RT metrics using an accessible system at all levels of sports participation when a concussion is suspected or during return to play decisions. With this in mind, this study aimed to investigate if inertial sensors can be used in conjunction with the clinical DTS for RT assessments in out-of-laboratory environments. The validity and inter-rater reliability were assessed by comparing inertial sensors to the standard motion capture laboratory-test environment during multiple positions and with dual-task conditions. It was hypothesized that when performing the drop-stick test in sitting and standing positions and on different surfaces (standing on foam versus firm surface), both with and without dual-tasking, the drop-stick test with sensors would demonstrate good validity when compared with gold-standard motion capture systems in the laboratory.Fourteen healthy adults  with a cohort mean (±standard deviation) age 24.7 (2.6) in years; height 173.4 (7.9) in cm; mass 75.3 (11.9) in kg; BMI 25.0 (3.8) participated. King-Devick’s scores were 40.0 (6.3) seconds. Participants were recruited using Chapman University IRB-approved flyers that were posted and distributed to the general public and around the campus at a local university. Participants were excluded if they had a recent  musculoskeletal injury of lower body survey, had uncorrected visual problems or inner ear problems including dizziness and vertigo, had sustained a concussion within the past two months , had received treatment for a respiratory infection within the last 30 days, had a diagnosed pulmonary disease or any other physical or systemic disorder or spinal deformities that may affect breathing, were color blind, and were pregnant or thought they might be pregnant.A mechanistic observational prospective cohort case-control study design was developed to assess the clinical drop-stick test utilizing an innovative protocol using single-and-dual-task conditions ,31,32,332). The accelerometer’s sensitivity was 31.2 LSB/g, and the gyroscope’s sensitivity was 14.375 LSB/s. Another sensor was affixed at the base of the wrist of the participant. The sampling frequency of the sensor and camera motion capture was set as 100 Hz.The drop-stick test system was developed similarly to previous studies to assess the clinical RT ,26. The In this condition, participants were asked to simultaneously monitor DTS and a computer monitor. A MATLAB (MATLAB (2022a), The MathWorks Inc., Natick, MA, USA) app was built, which could acquire real-time sensor data using Bluetooth and trigger a change in the color of GUI, either ‘green’ or ‘red.’ The algorithm to produce dual tasks on the computer monitor using the MATLAB app was provided, as shown by the flowchart in Drop-stick test system (DTS)-based data collection was conducted in a single laboratory session lasting approximately 60 min. All participants who met the inclusion and exclusion criteria provided informed consent as approved by the Chapman University Institutional Review Board (IRB). After demographic data was collected , participants completed the King-Devick (K-D) ,40 assesK-D is one of the most efficient and accurate sideline concussion screening tools with high sensitivity (0.86–1.00) and specificity (0.90–0.94) ,42. ThisOnce informed consent was obtained, and demographic data and K-D were collected, the participants entered the laboratory and were instrumented in preparation for the clinical stick-drop test. The researcher collecting the specific laboratory metric tests was blinded to the participants’ K-D scores.The stick-drop test occurred under single-task testing conditions while standing and sitting on a force plate. A total of ten trials were collected under each single-task condition.During dual-task testing, participants visualized a computer screen placed 1.2 m in line of sight with their catch hand . They weParticipants were instructed to stand on foam and perform catch during drop-stick tests.Participants were instructed to stand on a firm surface during the drop-stick catch protocol testing.Participant RTs were collected for the drop-stick test during the sitting condition. At least ten trials were collected for each situation. Furthermore, the drop-stick had measuring tape secured to it, allowing the researcher to manually read distance measures in inches converted to centimeters for analysis to compare to measurement data provided by motion capture and IMUs.RT Evaluations: Data from the infra-red marker and sensor located on the base of the stick, as shown in Inertial Sensor Event Identification: Five temporal events were identified from vertical acceleration from the sensor affixed to the drop stick . The senp < 0.05). The inter-instrument reliability between DTS and camera-based motion capture systems was tested using intraclass correlation coefficient (ICC) assuming average fixed raters , which measures inter-instrument reliability in terms of consistency [Data Analysis and Statistics: The mean RT from each of the ten trials for every condition was used for data analysis. The mean RT of all participants was tested for normality using the Shapiro-Wilk test. Statistical significance was calculated at a 95% confidence level  outcome measure is comparable to computer-based RT measures. It is both valid, with criterion and constructs validity, and reliable for measuring performance differences in concussed and non-concussed individuals, including under dual-task conditions ,47,48. TThe RT measurements from the fourteen participants using the camera system and sensor-based DTS showed moderate to good inter-instrument reliability with an overall ICC of 0.82 (95% CI 0.78–0.85) . There wThe DTS was validated for RT measurements for multiple conditions, including (i) standing, (ii) sitting, (iii) standing on foam, (iv) standing on a firm surface, (v) foam, and no foam standing. The DTS showed good consistency for standing with an ICC of 0.78 (95% CI 0.71–0.83), moderate consistency for sitting with an ICC of 0.70 (95% CI 0.56–0.80), good consistency for a single task with an ICC of 0.79 (95% CI 0.75–0.83), good consistency for dual tasking with an ICC of 0.75 (95% CI 0.64–0.83), very good consistency for standing on foam with an ICC of 0.88 (95% CI 0.84–0.91), and good consistency for standing on a firm surface with an ICC of 0.78 (95% CI 0.71–0.83) .Bland and Altman plots can quantify the agreement between two quantitative measurements by constructing limits of agreement. If the bias in the Bland–Altman plot is zero, the measurements obtained by the camera system or DTS gave the same results. But any measurement by instruments will have some degree of error, generating a variability of differences in the Bland–Altman plot. Most of the data points remained over the lower LOA. Thus, the camera system reported larger RTs compared with the DTS in standing (foam and firm surface), sitting, and single and dual-task conditions. This study’s temporal data differences between the camera and sensor systems are not unique. Previously reported temporal events of human movements such as sit-to-stand , sit-to-Although the camera-based motion capture is considered the gold standard for movement quantification, it requires an update to meet the evolving needs of on-site concussion assessment during play. With the recent COVID-19 pandemic, the assessment and rehabilitation paradigms are shifting to out-of-clinic environments. Platforms, such as the DTS, provide an objective measurement of functional RT while allowing the healthcare provider to assess concussion effects reliably over time. Moreover, integrating the DTS with a smartphone via wireless Bluetooth connectivity and automatic RT evaluations could provide quick assessment or comparative reports on the smartphone. The current study provided the first evidence to support this simple RT assessment using the DTS with inertial sensors (IMUs) under different conditions and during dual-tasking as a reliable measurement tool compared with the gold-standard motion capture.The current study is innovative and provides a unique insight into the applicability of an RT testing protocol using inertial sensors under single and dual-task conditions, which are translatable into the training room. Lempke and coworkers demonstrated that the clinical stick drop and computer-based RT tests were comparable but suggested that the stick drop RT may be a better indicator when making a return to play decisions, especially under dual-task test conditions . They alThis study had several limitations, and the findings should be interpreted accordingly. The sample size was small and only reflected DTS reaction times within a young, healthy population. Since this study focused on the reliability of two measurement systems, gender was not selected as an independent factor. This may explain some of the outliers and variances within the results. However, subjects acted as their own control by performing the same tasks on both measurement systems. Previous research using visual stimuli RT assessments is mixed, with some findings supporting increased RT in males  and otheIn recent years, concussions have been a growing issue, partly due to increased awareness and an improved understanding of the condition . A concuThe availability of an inexpensive, portable, reliable, objective RT assessment performed under single and dual tasking is essential for creating safe athletic environments. RT can be measured at baseline pre-season testing, used for those suspected of sustaining a concussion, or for return-to-play decisions. Since RT reflects cognitive processing when performed under dual-task conditions, it may assess inhibitory function, which is vital to executive functions of the brain, especially for those who have suffered a concussive event . To provThe findings of this study suggest that the DTS protocol and testing apparatus are reliable tools to measure RT and may be translatable to the field or training room for concussion assessment. Inertial sensors (IMUs) utilized under the protocol conditions during single and dual-task conditions strengthen claims for reliably measuring RT. These findings are promising for clinicians and researchers using RT to assess those suspected of experiencing a concussion and to make decisions regarding injury, recovery, and safe return to sports. The development of the DTS, which has the potential for accessible and reasonable measures for RT, increases the safety of environments at all levels of athletics. Future research will investigate DTS validity and reliability in clinical populations with mild TBI outside lab environments.


People living with HIV (PLWH) may have a poorer prognosis with COVID-19 infection and are an important population for COVID-19 vaccination. We assessed the willingness and reasons for COVID-19 vaccine acceptance or hesitancy among PLWH in South Africa.We conducted a cross-sectional study consisting of telephone interviews with a randomly selected subset of participants enrolled in a prospective observational cohort study evaluating a decentralized antiretroviral therapy (ART) delivery program in South Africa. Questions assessed willingness to accept a future COVID-19 vaccine, concerns regarding COVID-19 vaccination, and overall vaccine confidence. Interviews were conducted between September 2020 and January 2021. We evaluated participant demographics, sources of COVID-19 information, stigma and medical mistrust, uptake of non-pharmaceutical interventions, and socioeconomic impacts of the COVID-19 pandemic as potential covariates of willingness to accept vaccination.We completed interviews with 213 participants; 153 (72%) were female, median age 35y, and 100 (47%) had completed secondary school. Among the participants, 121 (57%) were willing to accept future vaccination, 46 (22%) were unsure, and 45 (21 %) stated they did not intend to be vaccinated. Fear of side effects, reported by 42 (20%), was the most common concern about COVID-19 vaccination. Older age was associated with willingness to accept vaccination , while higher medical mistrust related to COVID-19  and use of social media for COVID-19 information  were associated with lower willingness to accept vaccination.In this cohort of PLWH in South Africa, over half were willing to accept COVID-19 vaccination, although a substantial proportion remained unsure or were not willing to be vaccinated. Public health messaging should emphasize the safety and efficacy of COVID-19 vaccination and address misinformation and medical mistrust among PLWH. Ongoing efforts to ensure access to COVID-19 vaccines for vulnerable populations are crucial. The novel coronavirus disease 2019 (COVID-19) pandemic has created an unprecedented global health challenge . VaccinaWhile the first COVID-19 vaccine candidates showing efficacy in clinical trials have been rolled out worldwide, significant concern remains about vaccine supply, delivery, and uptake in low- and middle-income countries. The World Health Organization (WHO) Strategic Advisory Group of Experts (SAGE) Working Group on Vaccine Hesitancy defines “vaccine hesitancy” as a “delay in acceptance or refusal of vaccines despite availability of vaccine services” . VaccineVaccine hesitancy differs across time, place, and specific vaccines being offered . UnderstThis was a cross-sectional study conducted through telephone interviews between 25 September 2020 and 8 January 2021, prior to the availability of COVID-19 vaccines in South Africa.31 year, and virologically suppressed as per program guidelines) in nine public sector clinics offering the CCMDD program in the urban township of Umlazi [We contacted a subset of participants enrolled in a prospective observational cohort study evaluating a decentralized ART delivery program in KwaZulu-Natal province, South Africa. The parent study recruited participants meeting eligibility criteria for the national Chronic Medicine Dispending and Distribution (CCMDD) program . We defined ‘recommended’ changes in daily activities as those falling within the recommendations of the National Department of Health of South Africa –22 and dWe used descriptive statistics  to report baseline and COVID-era participant characteristics, sources of information on COVID-19, levels of COVID-19 stigma and medical mistrust, and responses to questions on COVID-19 vaccination and general vaccine confidence. For modelling measures of medical mistrust and stigma related to COVID-19, we categorized data into above and below the median to use the two variables consistently in the analysis. This also allows for easy interpretation of the effect of the variables. We determined internal consistency among the questions on COVID-19 vaccine confidence using Cronbach’s alpha. A Cochran-Armitage test of trend was used to assess the relationship of willingness to accept vaccination with the summary COVID-19 vaccine confidence measure. We used univariate and multivariable logistic regression to assess predictors of willingness to accept COVID-19 vaccine (as defined by an answer of “yes” to “do you intend to accept future COVID-19 vaccination for yourself?”). Factors with p<0.05 in univariate logistic regression models were included in a multivariable model in addition to age and gender, which were pre-specified. All reported p-values were two-tailed, and p<0.05 was considered statistically significant. Analyses were conducted using SAS software .The study protocol was approved by the Biomedical Research Ethics Council of the University of KwaZulu-Natal (Protocol BE092/18) and by the Partners Healthcare Institutional Review Board (Protocol 2017P001690).Two hundred and thirteen participants consented to and completed the interview. Among the participants, 153 (72%) were female, 212 (99.5%) identified their ethnicity as Black, median age was 35 , and 100 (47%) had completed secondary school. Median time since ART initiation was 2.0 years (IQR 1.0–4.0). The minority  were employed at enrolment, all (100%) reported good, very good, or excellent ability to take HIV medication, and only 27 participants (13%) reported any competing needs at baseline .Nearly three-quarters of participants reported radio  and television  as their main sources of information on COVID-19, followed by clinic materials or clinic staff  and social media  . The medOne hundred and seventy-eight (84%) participants were not vaccinated against influenza in the last influenza season, with 51 (29%) stating that they did not think the influenza vaccine was needed. More than half of all participants  responded that they are willing to accept future COVID-19 vaccination for themselves, with an additional 46 (22%) stating they were unsure if they would accept COVID-19 vaccination . SimilarRegarding general vaccine confidence, a majority of participants  strongly agreed that vaccines are important for children to have, 163 (77%) strongly agreed that overall they think vaccines are safe, 165 (79%) strongly agreed that vaccines are effective, and 180 (90%) strongly agreed that vaccines are compatible with their religious beliefs .In a multivariable analysis controlling for gender, older age was associated with willingness to accept COVID-19 vaccination . Reporting social media as a source of information on COVID-19 was associated with lower willingness to accept vaccination , as was having a summary medical mistrust score at or above the median  . COVID-1In a cohort of people living with well-controlled HIV in South Africa accessing ART through a decentralized medication distribution program, over half of individuals reported that they would be willing to accept a COVID-19 vaccine. Our sample of PLWH on ART is 72% female, which is similar to the 69% female national population of PLWH on ART in South Africa . IncreasWhile slightly more than half of participants in this cohort were willing to accept future COVID-19 vaccination for themselves, this rate is inadequate for the achievement of herd immunity in South Africa, which will require the vaccination of at least 70% of eligible adults. Other studies have reported a wide range of willingness to accept COVID-19 vaccination in sub-Saharan Africa, from 15% in Cameroon  to 56% iSeveral studies among PLWH in high-income countries have found similar or lower rates of willingness to accept COVID-19 vaccination compared to our study. In a study conducted among Black American PLWH early in the COVID-19 pandemic, 32% stated that if a vaccine were available, they would not want to get it, and 54% endorsed any vaccine hesitancy belief, which was in turn associated with higher levels of medical mistrust . In FranFear of side effects from a COVID-19 vaccine was the most common concern regarding COVID-19 vaccination in this cohort, similar to previous studies of COVID-19 vaccine hesitancy and intent ,31,33–35Results of this study provide additional insight into determinants of willingness to accept COVID-19 vaccination among PLWH. We found that PLWH who are older are more likely to accept vaccination, consistent with previous findings regarding COVID-19 vaccine intent among people with and without HIV . This fiMore than eighty percent of participants were not vaccinated against influenza in the last influenza season, despite a recommendation from the South African Department of Health for annual influenza vaccination for PLWH . In lighThis study is among the first to evaluate willingness to accept COVID-19 vaccine among PLWH in South Africa, which has the world’s largest population of PLWH . UnderstAmong people living with HIV enrolled in a decentralized ART delivery program in South Africa, over half of individuals reported that they are willing to accept a COVID-19 vaccine. Fear of side effects from a COVID-19 vaccine was the most common concern regarding COVID-19 vaccination. Higher age was associated with willingness to accept COVID-19 vaccination, while higher medical mistrust related to COVID-19 and obtaining information on COVID-19 from social media were associated with lower odds of willingness to accept COVID-19 vaccination. While nearly half of participants were either unsure or not willing to accept COVID-19 vaccination, overall vaccine confidence was high. Our results suggest that communication strategies reassuring safety and efficacy of COVID-19 vaccines and addressing sources of misinformation on COVID-19 will be important to support COVID-19 vaccine uptake in this population.


To investigate the effects of the U.S. Preventive Services Task Force’s (USPSTF) 2012 recommendation against prostate-specific antigen (PSA)-based screening for prostate cancer on survival disparities based on insurance status. Prior to the USPSTF’s 2012 screening recommendation, previous studies found that insured patients with prostate cancer had better outcomes than uninsured patients.Using the SEER 18 database, we examined prostate cancer-specific survival (PCSS) based on diagnostic time period and insurance status. Patients were designated as belonging to the pre-USPSTF era if diagnosed in 2010–2012 or post-USPSTF era if diagnosed in 2014–2016. PCSS was measured with the Kaplan–Meier method, while disparities were measured with the Cox proportional hazards model.During the pre-USPSTF era, uninsured patients experienced worse PCSS compared to insured patients . This survival disparity was no longer observed during the post-USPSTF era as a result of decreased PCSS among insured patients combined with unchanged PCSS among uninsured patients .Although the underlying reasons are not clear, the USPSTF’s 2012 PSA screening recommendation may have hindered insured patients from being regularly screened for prostate cancer and selectively led to worse outcomes for insured patients without affecting the survival of uninsured patients. Prostate cancer is the most commonly diagnosed cancer and the second most common cause of cancer death for men in the United States with an estimated 34,130 deaths per year . MoreoveThe potential benefits of prostate-specific antigen (PSA)-based screening to reduce mortality has been debated largely due to the contradicting results of two major randomized controlled trials. In contrast to the European Randomized Study of Screening for Prostate Cancer (ERSPC) that reported a survival benefit to screening, the United States Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial did not find such a survival benefit. Moreover, the benefits of prostatectomy for PSA screen-detected prostate cancer remain unknown, because while the Scandinavian Prostate Cancer Group Study Number Four SPCG-4) reported a survival benefit for prostatectomy, the Prostate Cancer Intervention Versus Observation Trial (PIVOT) did not [ reportedThus, the United States Preventive Services Taskforce (USPSTF) recommended against PSA-based screening for prostate cancer in May 2012 due to the aforementioned clinical trials and studies that reported that screening yielded few benefits in mortality and many potential harms such as complications from biopsies and subsequent treatment as well as the risk of overdiagnosis and overtreatment . This reWhile previous studies have examined the effects of the USPSTF’s screening recommendation on racial disparities , to our This study examined patients who were at least 40-years-old and diagnosed with prostate cancer from the Surveillance, Epidemiology, and End Results (SEER) 18 registries census tract-level socioeconomic status database submitted in November 2018. This database contains patient information from the Alaska Native Tumor Registry, Connecticut, Detroit, Atlanta, Greater Georgia, Rural Georgia, San Francisco-Oakland, San Jose-Monterey, Greater California, Hawaii, Iowa, Kentucky, Los Angeles, Louisiana, New Mexico, New Jersey, Seattle-Puget Sound, and Utah.Sociodemographic, clinicopathology, and treatment-related variables evaluated include insurance status, age, race, PSA, biopsy Gleason score (bGS), summary stage, and treatment with prostatectomy or radiotherapy. The SEER-designated summary stage incorporates both clinical and pathological information.Three insurance categories were studied: insured, uninsured, and Medicaid. Given that Medicaid provides coverage for low-income individuals or those with disabilities, Medicaid and insured patients were treated as distinct groups to isolate potential confounding socioeconomic effects of Medicaid’s eligibility criteria on PCSS.Given our rationale that the screening recommendation would result in more advanced prostate cancer presentation and thus increased prostate cancer-specific mortality, the primary study outcome was prostate cancer-specific survival (PCSS) based on diagnostic time period and insurance status. Patients were designated as belonging to the pre-USPSTF era if diagnosed in 2010 to 2012 or post-USPSTF era if diagnosed in 2014 to 2016. 2013 was considered the buffer year to account for the time it would take for the new USPSTF recommendations to take effect.PCSS was measured with the Kaplan–Meier method, while disparities were measured with the log-rank test and Cox proportional hazards model. Since patients diagnosed in 2016 could only be followed for up to 36 months, all survival curves were restricted to time intervals of 36 months. A sub-analysis by stage was also conducted, since 36 months may not have been a sufficient follow-up time to observe survival disparities for earlier stages of prostate cancer. For insurance status, hazard ratios were adjusted for age group , race, PSA group , biopsy Gleason score , and treatment with prostatectomy and/or radiotherapy or no local therapy. Changes in the distribution of insurance status were analyzed with the Pearson chi-square test, as changes in PCSS may be partially attributable to changes in the distribution of insurance status. Temporal changes in the distribution of patient characteristics between insurance groups were analyzed with a multinomial logistic regression using insured patients as the reference group to determine whether differences in patient characteristics from the pre- to post-USPSTF era were different between insured and Medicaid patients as well as between insured and uninsured patients. All statistical analyses were conducted using Stata/SE 15.0 .Our study cohort was composed of 282,266 patients at least 40-years-old diagnosed with prostate cancer between January 2010 to December 2012 (pre-USPSTF) and January 2014 to December 2016 (post-USPSTF). There were 152,416 (54.00%) patients in the pre-USPSTF era and 129,850 (46.00%) patients in the post-USPSTF era. The median age of patients in the pre-USPSTF era was 65-years-old (IQR: 59–72-years-old), while that of patients in the post-USPSTF era was 66 (IQR: 60–72-years-old). Median follow-up time was 36 months (IQR: 36–36 months) in the pre-USPSTF era and 16 months (IQR: 8–26 months) in the post-USPSTF era . Specifically, insured patients decreased from 92.64 to 91.45%, while uninsured patients decreased from 1.90% to 1.33%. Contrastingly, the percentage of Medicaid patients increased from 5.47% to 7.22%. Additionally, patients in the post-USPSTF era presented with higher PSA, biopsy Gleason score (bGS), and higher summary stage, features consistent with more advanced disease . These post-USPSTF patients were also less likely to be treated with prostatectomy or radiotherapy . Among these patients who did not receive local therapy, the proportion of those with regional and distant cancer increased across the eras from 3.11 to 4.08% and 8.89 to 12.71%, respectively, while those with localized disease decreased from 88.00 to 83.20%  and 45.12%  were in the pre- and post-USPSTF eras, respectively. Of the Medicaid patients, 47.63%  and 52.37%  were in the pre- and post-eras, respectively. Of the uninsured patients, 63.15%  and 36.85%  were in the pre- and post-USPSTF eras, respectively.From the pre- to post-USPSTF era, the changes in PSA between insured and uninsured patients as well as insured and Medicaid patients were statistically significant with insured patients experiencing a greater increase in those with a PSA greater than 10 ng/ml but less than or equal to 20 ng/ml than uninsured and Medicaid patients . Compared to insured patients, however, uninsured patients experienced a greater increase in those with a PSA greater than 20 ng/ml. Insured patients also presented with higher bGS than uninsured and Medicaid patients with the proportion of those with bGS less than or equal to 6 decreasing from 45.40%  to 34.23% , bGS of 7 increasing from 38.00%  to 42.45% , and bGS greater than or equal to 8 increasing from 16.60%  to 23.32%  . Lastly, while insured and uninsured patients experienced similar shifts in local treatment from the pre- to post-USPSTF era , insured patients experienced a greater decrease in prostatectomy and radiotherapy relative to Medicaid patients .During the pre-USPSTF era, uninsured patients experienced worse PCSS than insured patients (Fig. Insured patients across all stages of prostate cancer experienced a statistically significant decrease in survival from the pre- to post-USPSTF eras, while Medicaid and uninsured patients did not experience a survival change Fig. . In an aIn the present study, we show that patients in the post-USPSTF era presented with more adverse clinicopathologic features, such as higher PSA, bGS, and stage. In addition, the disparity in prostate cancer survival between the insured and uninsured disappeared in the post-USPSTF era due to the worse outcome in the insured group. The decrease in PCSS of insured patients can largely be explained by the decrease in PCSS of insured patients with low PSA. Importantly, we found that the black race was not a predictor of worse prostate cancer survival outcome when insurance status was adjusted.Our observation of worse prostate cancer outcome following the 2012 USPSTF's recommendation against PSA-based screening is consistent with that of previous studies, which report that the USPSTF’s 2012 PSA screening recommendation has had profound effects on the presentation and management of prostate cancer. Our previous work found that prostate-cancer specific survival for all prostate cancer patients worsened after 2012 . A separ., however, found that among privately insured patients aged 40–64, the rate of PSA testing decreased from 27.6% in 2010–2012 to 25.9% in 2014, suggesting that the recommendation may have contributed to a decrease in PSA screening rates among insured patients [The disappearance of PCSS disparity between insured and uninsured patients after the USPSTF’s 2012 screening recommendation occurred due to a decrease in PCSS among insured patients combined with no change in PCSS among uninsured patients. This finding suggests that in the pre-USPSTF era, insured men were more likely to be screened for prostate cancer than uninsured men, likely due to more consistent urologic care as well as insurance coverage of PSA screening. Aghdam and colleagues reported that insurance status was the most protective factor against presenting with metastatic prostate cancer . Kearns patients –24. AddiIn contrast to the survival disparity between insured and uninsured patients that disappeared from the pre- to post-USPSTF era, that between insured and Medicaid patients did not significantly change, suggesting that the decrease in PCSS among Medicaid patients approximated that of insured patients. Moreover, Medicaid patients consistently experienced worse PCSS than insured patients across both eras and even uninsured patients in the post-USPSTF era. Previous studies have found that compared to insured patients, Medicaid patients with prostate cancer are more likely to present with metastatic disease and have higher prostate cancer-specific mortality . Suh andIn the sub-analysis stratified by stage, the only survival disparities between insured and uninsured patients in the pre-USPSTF era were observed in those with distant prostate cancer. This finding may be explained by the follow-up period of 36 months, as this timeframe may not be long enough to observe survival disparities among patients with localized and regional prostate cancer given that the 5-year survival of these patients has been reported to be up to more than 99%. In contrast, given the 5-year survival rate of 31% for distant disease, a 36-month follow up time was likely sufficient to observe changes in survival disparities for those with distant prostate cancer . Thus, gPreviously, we have reported a survival disparity between Whites and Blacks in the pre-USPSTF era . While tThe main strength of our study was the use of a large population-based database, which enabled the analysis of real-world trends and outcomes. Several limitations of our study include those inherent to the SEER database including the lack of screening data on patients with prostate cancer in SEER, the increased censoring of patients in the post-USPSTF era compared to the pre-USPSTF era, the assumption that insurance coverage of patients has remained consistent over time, the short follow-up period of 3 years, and the lack of SEER data on actual prostate cancer screening rates. Additionally, while outcomes such as biochemical-recurrence-free survival or metastasis-free survival may have been more appropriate, the SEER database does not include these outcomes, making PCSS the best available outcome. Using PCSS as the primary outcome and a follow-up period of only 3 years may have resulted in the number of patient deaths being too small to detect a survival disparity in the post-recommendation era. However, we did apply the same primary outcome and follow-up period for patients in the pre-USPSTF era and still found a survival disparity, indicating that a factor other than the small number of observed patient deaths is responsible for the disappearance of a survival disparity between insured and uninsured patients. Regardless, a difference in PSA screening rates is not necessarily the only change that occurred between eras. Thus, this study should be considered a hypothesis-generating study from which future studies may test these hypotheses on additional databases.The USPSTF’s 2012 PSA screening recommendation may have had unintended, detrimental effects on survival disparities based on insurance status, suggesting that alternative approaches to screening may be necessary for improved survival among both insured and uninsured patients.


Clustering stroke patients with similar characteristics to predict subsequent vascular outcome events is critical. This study aimed to compare several clustering methods, particularly a deep neural network-based model, and identify the best clustering method with a maximally distinct 1-year outcome in patients with ischemic stroke. Prospective stroke registry data from a comprehensive stroke center from January 2011 to July 2018 were retrospectively analyzed. Patients with acute ischemic stroke within 7 days of onset were included. The primary outcomes were the composite of all strokes (either hemorrhagic or ischemic), myocardial infarction, and all-cause mortality within one year. Neural network-based clustering models (deep lifetime clustering) were compared with other clustering models  and a conventional risk score  to obtain a distinct distribution of 1-year vascular events. Ultimately, 7,650 patients were included, and the 1-year primary outcome event occurred in 13.1%. The DLC-Kuiper UB model had a significantly higher C-index (0.674), log-rank score (153.1), and Brier score (0.08) than the other cluster models (SSC and DLC-MMD) and the SPI-II score. There were significant differences in primary outcome events among the 3 clusters  when the DLC-Kuiper UB model was used. A neural network-based clustering model, the DLC-Kuiper UB model, can improve the clustering of stroke patients with a maximally distinct distribution of 1-year vascular outcomes among each cluster. Further studies are warranted to validate this deep neural network-based clustering model in ischemic stroke. The risk of recurrent vascular events is substantially high within the first several months after an index stroke3. Therefore, research on the early prediction of recurrent stroke and identification of high-risk patients is of great importance. Several markers, including clinical, radiological and laboratory findings4 and scoring methods, such as Stroke Prognostic Instruments (SPI)-II or the Essen Stroke Risk Score (ESRS), have been reported to be associated with recurrent events7. SPI-II and ESRS are commonly used and reliable risk scores because they are relatively simple and intuitively expressed through clinically important variables. However, the applications of conventional risk scores were limited because complex interactions among diverse factors, such as large artery steno-occlusion, stroke subtypes, and initial stroke severity, might exist in real clinical settings. If more variables are used, the degree of risk can be distinguished more effectively, but it will be more complex than conventional indicators. Artificial intelligence might provide a technical solution to these difficulties.Stroke is one of the leading causes of mortality and disability in elderly patients9. The identification of similar clusters according to “time-to-event” also plays an important role in stroke care and future research. However, AI clustering models remain a relatively unexplored topic in stroke fields. Clinically, large datasets comprise phenotypically heterogeneous subpopulations—i.e., subsets of observations that cluster according to both covariates and time-to-event similarities. For example, in a clinical setting, identifying high-, medium-, and low-risk subpopulations equipped with accurate time-to-event estimates can allow more appropriate targeting of interventions, treatments or care delivery. Therefore the use of neural network-based models to identify heterogeneous subpopulations in acute ischemic stroke with different time-to-event risks  would be a crucial step toward precision medicine11.In recent years, AI techniques have been used in an increasing number of stroke-related studies. The main areas of AI stroke studies are outcome prediction, diagnosis and treatments12, semi-supervised clustering13, and supervised sparse clustering methods14. Recently, a neural network-based lifetime clustering model, DeepCLife, was introduced15 that could identify cluster assignments by directly maximizing the divergence between the empirical lifetime distributions of the clusters.Several lifetime clustering methods exist, such as traditional unsupervised clustering methods, including k-meansThis study attempted to show a clearer prognostic difference using more information and more variables than those used for SPI-II scores. Thus, a clustering method was used to create risk groups and compare them. Additionally, we compared several lifetime clustering methods, including the deep lifetime clustering (DLC) method, and assessed the performance of the clustering methods on a real-world survival dataset of patients with ischemic stroke.17. A total of 8,136 patients with ischemic symptoms who were admitted to our tertiary stroke center between January 2011 and July 2018 were initially screened. We included patients with ischemic stroke within 7 days of onset. The detailed eligibility criteria and patient selection flow chart are shown in Supplemental Fig. This study analyzed data from a single regional comprehensive stroke center registry of consecutive patients with acute ischemic stroke. This registry is part of the Clinical Research Collaboration for Stroke-Korea (CRCS-K) registryhttps://github.com/PurdueMINDS/DeepLifetimeClustering).Clinical information was obtained from the registry database. The registry has been collecting clinical information for the purpose of monitoring and improving the quality of stroke care since 2011, with the approval of the institutional review boards of our hospital. The use of the registry database and the supplemental review of medical records in this study were approved by the institutional review boards. A waiver for informed consent was provided because of study subject anonymity and minimal risk to the participants by an institutional review board at Chonnam National University Hospital. All the methods were performed in accordance with the relevant guidelines and regulations (Declaration of Helsinki). The corresponding author will provide the data, analytical methods, and study materials to other researchers upon reasonable request. Additionally, the GitHub website address for the DLC method is as follows: , myocardial infarction, and all-cause mortality within one year after the index stroke. Secondary outcomes consisted of the following individual events: (1) all types of recurrent stroke (ischemic and hemorrhagic) and (2) all-cause mortality. As previously describedThe clustering method is a type of unsupervised learning that consists of similar characteristics within a group and different characteristics between groups through the characteristics of individuals. This means that there are no actual labels, but clustering gives each object a new label (a risk-based cluster in the study). The criteria for dividing the degree of risk for the generated clusters are the Kaplan–Meier curve.5. To investigate the risk of 1-year vascular events, the SPI-II scores were categorized into 3 groups based on previous studies: low risk (0–3), medium risk (4–7), and high risk (> 8).As a reference in stroke patient clustering, the SPI-II scores were used. The SPI-II scores were retrospectively calculated as the sum score (0–15 points) of 7 clinical factors based on their predictive significance: congestive heart failure (3 points), diabetes mellitus (3 points), prior stroke (3 points), age > 70 years (2 points), stroke as the index event (2 points), severe hypertension (1 point), and coronary artery disease (1 point)12.K-means clustering is a representative clustering method that assigns a given datapoint to preset k clusters. The cluster is updated until no further cluster changes occur in the direction of minimizing the sum of squares of the center of each cluster and the distance between objects. However, K-means clustering can be used in continuous data. The method used for categorical data is called k-mode clustering, which replaces the means of clusters with modes. K-prototypes is a clustering method that combines K-means and K-modes methods for use in a mixture of continuous and categorical data13.SSC-Bair is a clustering model that groups using K-means by selecting covariates that affect the hazard ratio through Cox models15.DLC proceeds with survival clustering to utilize neural networks to optimize differences between empirical lifetimes. Neural networks are learned through covariates of observations, and the final layer of neural networks is allocated to one of the k-clusters through softmax layers. The empirical lifetime distribution is obtained from the Kaplan–Meier estimator using the assigned clusters, and the model is trained to maximize the difference in the survival distribution of the clusters. Because DLC does not assume a proportional risk, it can cross a lifetime curve. The objective function of the DLC model is as follows:p-value upper bound (Kuiper UB) and maximum mean discrepancy (MMD) as divergence measures19.Although many methods are available to measure divergence, sample sizes must be considered. The gradient calculation should be simple because it serves as an objective function of neural networks. Additionally, proportional hazards should not be assumed. We used and compared the Kuiper 20, which expresses the importance of variables by comparing predictions of situations with baseline values when there are certain values for a given feature.We applied the Shapley Additive ExPlanations (SHAP) method to verify the importance of variables for each cluster. SHAP is a unified framework for predictive interpretationp-values less than 0.1 through the Cox model, classifying them into three clusters through k-means clustering. DLC is a neural network-based clustering model that used 30 characteristics in this study  and PyTorch (version 1.6.0).The data are reported as percentages, means (standard deviations), or medians (IQRs), depending on the variable characteristics. Categorical variables were analyzed using Pearson’s chi-squared test or Fisher’s exact test, and continuous variables were analyzed using analysis of variance or the Kruskal–Wallis test, as appropriate. The following parameters had missing data that were substituted using median values: BMI (2.2%), creatinine (0.2%), hemoglobin (0.1%), white blood cell count (0.1%), and initial random glucose (0.7%). The metrics used to evaluate the clusters obtained from the methods were the concordance index, Brier score, and Log-rank score test. The results of the metrics represented the mean and standard deviation in 30 iterations. In addition, we also conducted conventional statistical analyses for associations of each cluster with 1-year vascular outcomes according to the cluster methods in the study cohort. A The current study was approved by the institutional review board at Chonnam National University Hospital.A waiver for informed consent was provided because of study subject anonymity and minimal risk to the participants by the institutional review board at Chonnam National University Hospital.Among 8136 patients with stroke registered in the registry during the study period, 7650 patients  were ultimately analyzed for the study  patients. Regarding individual outcomes, the 1-year cumulative event rates were 2.9% for recurrent stroke and 10.7% for all-cause mortality. MI occurred in 0.2% of patients.P-values < 0.001). As there are no actual labels of high-, medium-, and low risk in the unsupervised clustering method, the Kaplan–Meier curves for 3 clusters could generate risk groups based on the risk of 1-year vascular events. Therefore, Cluster 0 is judged as the high-risk group, Cluster 1 as the medium-risk group, and Cluster 2 as the low-risk group.The one-year vascular events of each cluster according to the clustering methods are shown in Table However, for the SSC-Bair method, the primary vascular outcome within 1 year was not significantly different among the 3 clusters. As a comparator, primary vascular outcomes within 1 year were significantly different among the SPI-II subgroups  -inspired techniques, but Cox PH-based approaches are limited by the proportional hazards assumptions.However, clustering based on risk-profiles in survival analysis is relatively unexplored in machine learning but would be critical in applications such as  decision making. Several conventional risk scores for stratifying the risk of stroke recurrence exist27. With recent advances in machine learning, deep learning methods have improved classical survival analysis methods by leveraging non-linear relationship between covariates, for improved time-to-event or risk score predictions. Compared with classic methods such as the k-prototype and SSC-Bair methods, the DLC-Kuiper UB model is robust to the modeling issues associated with the inability to observe termination signals and does not assume proportional hazards15. Our results, therefore, found that the neural network-based DLC-Kuiper UB method could produce similar clusters with maximally distinctive 1-year vascular outcomes by using patient information on real-world stroke datasets. However, this method may be limited because it does not investigate clustering based on the severity of outcomes. Further validations for stroke subgroups, may also be warranted to confirm the results. In addition, the ability of the DLC-Kuiper UB model to incorporate more clinical variables to obtain better performance may not indicate a better AI-based DLC-Kuiper UB algorithm at the algorithmic level.Neural network-based clustering methods could automatically consider the significance/interaction of individual clinical factors. Xia et al. proposed an outcome-driven, attention-based multi-task deep learning model for classification and subtyping in patients with acute coronary syndromeA treatment versus B treatment by randomized controlled trials. However, individually tailored treatment would eventually be needed in acute ischemic stroke due to diverse stroke mechanisms and different risks of recurrence. Before that is possible, identifying similar clusters in stroke patients would be helpful to determine which groups would be better in group-specific research, including treatment. Additionally, because ‘time-to-event’ would be unlabeled, clustering relies on unsupervised machine learning. If the examples are labeled such as stroke subtypes, then clustering becomes classification. Our study found that the DLC-Kuiper UB methods consistently clustered similar patients in both the training and test sets  compared with the classic methods. The results might be the basis for future research applying different treatments according to the different clusters.Better treatment has been determined by comparing the efficacy and safety of 28. Liverani et al. proposed a Dirichlet process mixture model suitable for cases where it is difficult to apply usual survival models due to multicollinearity and identified clusters using both censored survival data and predictors29. Chapfuwa et al. proposed a time-to-event prediction model with structured latent representations that can be clustered through a prior for infinite mixture of distributions30. Among these various methods, our study used DLC because it was judged that a neural network-based model that maximizes the divergence of the empirical lifetime distribution among clusters considering censored events would more clearly distinguish groups according to the risk levels of stroke patients.As mentioned in the Introduction, survival clustering is important but is not being actively studied. Nevertheless, new methods continue to be proposed. Mouli et al. proposed a survival clustering method based on a decision tree prior to DeepCLife33. In the future, however, the addition of imaging findings, including brain MRI and clinical features, might improve the clustering and prediction of outcomes in acute ischemic stroke. In particular, further studies should be conducted only on patients with large vessel occlusions because these cases require endovascular treatment.Our results additionally provide the level of contribution of specific input features selected from the model using the whole dataset as well as individual patient information by using SHAP. The features that contributed the most to clustering were the initial NIHSS score and age among all 3 clusters. Our results support previous studies, as NIHSS scores and age were very important factors in grouping stroke patients and predicting outcomesThere are several limitations in our study. First, our study had the inherent limitation of including data from only a single stroke center registry in South Korea. Further studies using multicenter, multiethnic or multiracial datasets are warranted. Although datasets were divided and analyzed into training, validation, and test sets, further research with independent cohorts is warranted. In addition, the dataset was maintained at a constant outcome ratio through stratified sampling for analysis, but this may not necessarily be the case with a new dataset. Nonetheless, a neural network-based cluster model was first applied to stroke patients from a real-world dataset. Second, as available data in the registry might be limited, the results have limited generalizability. External validation would be warranted. Third, the clinical implications of clustering in stroke patients should be further investigated. Therefore, further verification and research are warranted by conducting well-designed clinical research in the future.In conclusion, our study found that a neural network-based clustering method, the DLC-Kuiper UB model, could be a good tool for clustering stroke patients with similar 1-year vascular outcomes. Further studies are warranted to verify and validate this AI-based clustering model in ischemic stroke.Supplementary Information.