BackgroundPrognostication plays a pivotal role in critical care medicine. Its importance is indisputable in the management of coronavirus disease 2019 (COVID-19), as the presentation of this disease may vary from docile, self-limiting symptoms to lethal conditions. Amid the COVID-19 pandemic, much emphasis was initially placed on molecular and serological testing. However, it was realized later that routine laboratory tests also provide key information in terms of the severity of the disease and thus could be used to predict the outcome of these patients.MethodologyThe aim of our study was to evaluate the biochemical parameters as prognostic markers in severely ill COVID-19 patients. We carried out a retrospective, case-control study. The study population was comprised of all severely ill COVID-19 patients admitted between October 2020 and January 2021 at our level 3 COVID hospital. Cases were defined as the patients who expired despite treatment and all resuscitative measures as per the standard operating procedures (SOPs) of our COVID intensive care unit (ICU) while controls were defined as the patients that were transferred out of the COVID ICU for further recovery. The detailed history, findings of physical examination, vitals recorded by point of care testing (POCT) devices at our ICU, clinical diagnosis, and the results of the biochemical analysis were recorded in a specially designed pro forma. The biochemical parameters recorded at the time of admission were compared between the groups of controls and cases in order to evaluate their role as predictors of mortality using appropriate statistical methods. P-values less than 0.05 were considered statistically significant. For all the parameters that showed a statistically significant difference, receiver operating characteristics (ROC) analysis was done to assess the utility of biochemical parameters as predictors of mortality or survival. Areas under the curve (AUCs) of 0.6 to 0.7, 0.7 to 0.8, 0.8 to 0.9, and >0.9 were considered acceptable, fair, good, and excellent for discrimination, respectively.ResultsOf the 178 severely ill COVID-19 patients enrolled in the study, 86 were controls and 92 were cases . Serum urea (p<0.0001), creatinine (p=0.0019), aspartate transaminase (AST) (p=0.0104), lactate dehydrogenase (LDH) (p=0.0001), procalcitonin (PCT) (p=0.0344), and interleukin 6 (IL-6) (p=0.0311) levels were significantly higher (p<0.05), while total protein (p=0.0086), albumin (p<0.0001), and indirect bilirubin (p=0.0147) levels were significantly lower (p<0.05) in cases as compared to controls. The difference was statistically insignificant (p>0.05) for serum sodium, potassium, total and direct bilirubin, globulin, alanine transaminase (ALT), alkaline phosphatase (ALP), D-dimer, and ferritin. On ROC analysis, urea was fair (AUC=0.721), creatinine (AUC=0.698) and IL-6 (AUC=0.698) were acceptable predictors of mortality, while albumin (AUC=0.698) was an acceptable predictor of survival in severely ill COVID-19 patients during their intensive care stay.ConclusionUnderstanding the pathophysiological changes associated with the severity of COVID-19 in terms of an alteration of biochemical parameters is a pressing priority. Our study highlights the importance of routine laboratory tests in predicting outcomes in severely ill COVID-19 patients. Prognostication plays a crucial role in the management of coronavirus disease COVID-19) 9 1-8]. . 8]. It . . 8]. ICOVID-19 is an infectious disease caused by severe acute respiratory syndrome (SARS ) coronavirus 2 (CoV-2) ,4. MortaLaboratory tests are particularly useful in validating a diagnosis, predicting disease severity, and monitoring disease progression in patients with infectious diseases like COVID-19 -18. TimeAn observational, retrospective, case-control study was conducted after obtaining approval from the Institutional Ethics Committee (IEC) of Shri Ram Murti Smarak Institute of Medical Sciences. We utilized data from all the patients admitted to the COVID intensive care unit (ICU) of our hospital, Shri Ram Murti Smarak (SRMS) Institute of Medical Sciences (IMS), Bareilly, Uttar Pradesh, India, from 1st October 2020 to 31st January 2021. As per the standard operating procedures (SOPs) of our institute, patients with clinical syndromes associated with COVID-19 infection (referred from the emergency department or wards of other departments) were admitted to our COVID ICU. These patients were screened (based on the following inclusion and exclusion criteria) for their eligibility as study participants . Only seThe following diagnostic definitions were used to define severely ill COVID-19 patients:1. COVID-19 PatientsDefined as patients with clinical syndromes associated with COVID-19, who tested positive for either real-time-polymerase chain reaction (RT-PCR) TrueNat or Rapid Antigen Test (RAT) for COVID-19 ,17.2. Severely Ill COVID-19 PatientsOur ICU follows the "Revised Guidelines on Clinical Management of COVID-19", issued by the Government of India Ministry of Health & Family Welfare Directorate General of Health Services (EMR Division) to define severely ill COVID-19 patients ,16,18,192) less than 90 percent (%) on room air [(A) Severe pneumonia: Defined as a patient suspected of having respiratory tract infection, with one or more of the following: 1) Respiratory rate greater than 30 breaths per minute; 2) Severe respiratory distress; 3) Peripheral capillary oxygen saturation  less than 200 (millimeters of mercury) mmHg or fraction of inspired oxygen (FiO2) less than or equal to 300 mmHg (with positive end-respiratory pressure (PEEP) or continuous positive airway pressure (CPAP) more than or equal to 5 centimeters (cm) of water (H2O)); (b) When PaO2 is not available, SpO2 or FiO2 less than or equal to 315; (c) Respiratory distress in non-ventilated patients [(B) Acute respiratory distress syndrome (ARDS): Defined as follows: 1) The development of new or worsening respiratory symptoms within one week of known exposure; 2) Bilateral opacities not explained by chest imaging studies; 3) Respiratory failure caused by factors other than cardiac failure or fluid overload; 4) Mild, moderate or severe ARDS: Defined as (a) Partial pressure of oxygen in arterial blood  Sepsis: Defined as a dysregulated host response to infection resulting in life-threatening organ dysfunction, diagnosed by: 1) Symptoms like altered mental status, difficult or fast breathing, or skin mottling; 2) Signs like low oxygen saturation, reduced urine output, fast heart rate, weak pulse, cold extremities or low blood pressure; 3) Laboratory evidence like coagulopathy, thrombocytopenia, acidosis, high lactate, or hyperbilirubinemia ,18,19.(D) Septic shock: Defined as persisting hypotension despite appropriate volume resuscitation, requiring vasopressors in order to maintain mean arterial pressure (MAP) more than or equal to 65 mmHg and serum lactate level less than 2 mmol/L ,16,18,19We excluded the following patients from our study: 1) Suspected cases of COVID-19 with no confirmatory laboratory results; 2) Laboratory confirmed COVID-19 cases that do not meet our criteria for severely ill COVID-19 patients; 3) Patients with insufficient data for further analysis, who denied the standard treatment protocol of our hospital or discharged against medical advice.Admission, history-taking, physical examination, analysis (done either by point of care testing (POCT) devices at the ICU or autoanalyzers at the laboratory), and management of all patients were done following the SOPs of our institute. Data required for the study were retrieved from patients’ clinical case files archived at the COVID ICU and our laboratory and hospital information systems (LIS and HIS), version 3. These endogenous information systems were developed by professionals of our engineering college  in 2017. Patients’ clinical profiles  and laboratory results of blood samples collected at the time of admission to the COVID ICU were recorded in an especially designed pro forma. All the biochemical parameters , aspartate transaminase (AST), alanine transaminase (ALT), alkaline phosphatase (ALP), lactate dehydrogenase (LDH), urea, creatinine, sodium (Na+), and potassium (K+)) were analyzed in the biochemistry section while D-dimer and procalcitonin (PCT) estimation was done at the pathology section of our Central Clinical Laboratory. Serum interleukin-6 (IL-6) and ferritin levels were measured in the Chemiluminescence Immunoassay (CLIA) section of our Central Research Laboratory. All parameters were analyzed following the SOPs of the respective sections of our laboratories. Table We categorized the data of the subjects based on their outcome (Survivors or Non-survivors) during their stay in the ICU. The survivors were designated as the control group while the non-survivors were the case group. We compared the results of the blood samples collected at the time of admission to the COVID ICU between the controls and cases to study the role of these biochemical markers in the prediction of mortality in severely ill COVID-19 patients.Categorical variables were described as frequency and percentages using demographics and continuous variables as mean and standard deviation (SD). Quantitative data were assessed for linearity using Kolmogorov-Smirnov analysis and tests of statistical significance (student’s unpaired t-test or Mann-Whitney-U test) were used depending upon the data type. Means for continuous variables were compared using independent group p-values in MedCalc software. The parameters with p-values less than 0.05 were considered statistically significant. Receiver operating characteristics (ROC) curve analysis was utilized  to assess the utility of biochemical parameters to predict mortality or survival. AUC of 0.6 to 0.7, 0.7 to 0.8, 0.8 to 0.9, and >0.9 were considered acceptable, fair, good, and excellent discrimination, respectively. Further, for all parameters with an AUC > 0.6, the sensitivity and specificity as predictors of survival or mortality (at specific cut-offs) were also assessed on the basis of the ROC analysis.Out of 231 critically ill patients admitted to our COVID ICU between October and January 2021, 178 severely ill COVID-19 patients were enrolled in the present study (based on our inclusion). We excluded 53 patients  based on our exclusion criteria. Out of 178 patients enrolled in the study, 134 (75.28%) were males and 44 (24.72%) were females. The youngest patient was 14 years old and the oldest patient was 97 years old, while the average age was found to be 62 years. The greatest number of patients (74.72%) belonged to the age group of 51-80 years. Figure We categorized 178 patients enrolled in our study based on their outcomes (survivors or Non-survivors) during their stay in the ICU. Table The biochemical parameters showed statistically significant differences (p<0.05) in their average values between the cases and controls groups. A statistically significant difference was observed for serum urea (p<0.0001), creatinine (p=0.0019), indirect bilirubin (p=0.0147), serum protein (p=0.0086), albumin (p<0.0001), AST (p=0.0104), LDH (p=0.0001), PCT p=0.0344), and IL-6 (p=0.0311)  curves were plotted for all the parameters that showed a statistically significant difference between the cases and controls. Below, we are reporting only the results of the ROC curve analysis for the parameters that were found to be predictors of mortality or survival based on AUC Table .The ROC curve for serum urea and serum creatinine as predictors of mortality has an area under the curve (AUC) of 0.721 (fair predictor) and 0.698 (acceptable), respectively. For a cut-off value of 52 mg/dl for serum urea, the sensitivity and specificity are 0.736 and 0.605, respectively, while for a cut-off value of 0.95 mg/dl for serum creatinine level, the sensitivity and specificity are 0.747 and 0.605, respectively, as a predictor of mortality , which puts it at the upper bounds of an acceptable score. For a cut-off value of 44.41 pg/ml of serum IL-6, the sensitivity and specificity are 0.703 and 0.605, respectively, as a predictor of mortality , which puts it at the upper bounds of an acceptable score. For a cut-off value of 3.25 g/dl of serum albumin, the sensitivity and specificity are 0.767 and 0.593, respectively, as predictors of survival Figure .In our study, critically ill COVID-19 patients showed distinct clinical, demographic, and laboratory features at the time of ICU admission. It was observed that men are more affected by COVID-19 in terms of both morbidity and mortality. Previous studies showed that this could be due to the expression of more angiotensin-converting enzyme 2 (ACE2) receptors in men than in women . MoreoveSerum urea (p<0.0001),creatinine (p=0.0019), AST (p=0.0104), LDH (p=0.0001), procalcitonin (PCT) (p=0.0344), and IL-6 (p=0.0311) levels were significantly higher (p<0.05) while total protein (p=0.0086), albumin (p<0.0001), and indirect bilirubin (p=0.0147) levels were significantly lower (p<0.05) in cases as compared to controls.The difference was statistically insignificant (p>0.05) for serum sodium, potassium, total and direct bilirubin, globulin, ALT, ALP, D-dimer, and ferritin.Abnormal renal function tests (higher levels of serum urea and creatinine) were reported in our study and the difference was found to be highly significant (urea (p<0.0001) and creatinine (p=0.0019)) between the controls and cases. The ROC curve for urea AUC=0.721) and creatinine (AUC=0.698) were found to be fair and acceptable predictors of mortality, respectively. For a cut-off value of 52 mg/dl for urea level, the sensitivity and specificity are 0.736 and 0.605, respectively, while for a cut-off value of 0.95 mg/dl for creatinine level, the sensitivity and specificity were 0.747 and 0.605, respectively  was found to be statistically insignificant . Many of these  are reported to have good prognostic value in several studies .Based on the findings of our study, we propose biochemical parameters as practical prognostic markers in severely ill COVID-19 patients due to the routine practice of their monitoring (owing to their easy availability) in these patients.We assume that the manifestation of clinical syndromes associated with severe COVID-19 is the actual time when the development of metabolic alterations responsible for death in severely ill COVID-19 patients is triggered. Based on this, we postulate that prognosticating at this triggering moment has to be both sensitive and specific ,16,18,19Based on the observations of this study, we believe that routine biochemistry parameters can be used as feasible prognostic markers in severely ill COVID-19 patients. With the help of easily available prognostic markers, COVID-19 patients at higher risk of poor outcomes can be appropriately managed -9. With The main strength of this study is its planning and execution at an ICU known for its quality services and adherence to SOPs and protocols. The well-planned study , the inclusion of results of the investigation done at the time of identification of clinical syndromes associated with severe COVID-19 in all study participants, and the quantitative analysis of all biochemical parameters using cutting-edge methods are some of the positive aspects of our study ,18,19,27The main limitation of this study is its retrospective design. However, we assume that the accuracy of the findings is acceptable, as we followed the SOPs prevalent in our institute for both biochemical investigations as well as the management of all patients enrolled in the study. The limited sample size and inclusion of patients with pre-existing diseases (including those who required invasive ventilation) are the major limitations. Studies with a larger sample size, with a special emphasis on understanding the role of other factors  on the biochemical profile of these patients and their correlation with adverse outcomes are direly needed. A study of these biochemical parameters in mild and moderate cases of COVID-19 as well would provide a better understanding of these biochemical alterations and their relation to the development of severe illness. Understanding these biochemical alterations would not only help in prognosticating the risk of development of severe illness but also guide the development of better treatment protocols ,19,28,29Renal dysfunction (elevated levels of serum urea and creatinine) and elevated serum IL-6 levels but lower serum albumin levels were found to be the best prognostic markers of in-hospital mortality of severely ill COVID-19 patients in our study population. We propose that the vigilant monitoring of these biochemical parameters can aid in the prognostication of COVID-19 and thus improve the clinical management of these high-risk patients. However, large‐scale, multicenter studies designed to evaluate the prognostic utility of these parameters in the prognostication of COVID-19, especially in severely ill patients, are required to validate our findings. It would really be very useful if these routine, time-tested, and feasible biochemical parameters were validated to be prognostic markers in severely ill COVID-19 patients.


Here, we walk through how the discrete, next generation process connects to the ordinary differential equation disease system of interest, linearized at the disease-free equilibrium. Then, we use linear algebra to develop a geometric explanation of why the spectral radius of the next generation matrix is an epidemic threshold. Finally, we work through a series of examples that help to build familiarity with the kinds of patterns that arise in parameter combinations produced by the next generation method. This article is intended to help new infectious disease modelers develop intuition for the form and interpretation of the basic reproduction number in their disease systems of interest.The basic reproduction number  The basic reproduction number for epidemic models, denoted The reader is assumed to have at least an introductory knowledge of differential equations and linear algebra. While a knowledge of linear algebra is not, strictly speaking, necessary to understand, develop, or simulate an epidemic model, it is both convenient for many analyses and necessary for developing a deeper understanding of the modeled disease systems. For students starting to explore infectious disease modeling, it is important to recognize that knowing how to invert and multiply matrices as one might in a basic linear algebra class does not necessarily translate into an understanding of what the quantities mean, and that having a heuristic understanding of the epidemiological concepts is not always enough to be confident in the mathematical techniques. In this paper, we break down the basic reproduction number and develop an understanding of it using basic linear algebra concepts. Then, we examine a series of simple models to understand why common patterns arise in the parameter combinations representing The basic reproduction number ion Fine .Mathematical modeling is often used to estimate the basic reproductive number for a disease system; in fact, the basic reproduction number is widely considered the most useful contribution of mathematics to epidemiology. Epidemic models are a class of models that represent disease processes—chiefly transmission and recovery, but possibly others—in mathematical form. There are many resources available for those interested in infectious disease modeling more broadly  who are susceptible (S) to disease, who are infectious (I), and who have recovered (R) over time t. This model has two parameters, the transmission rate S/N, in the rate. Recovery is a more straightforward process and is modeled as a linear rate N; the density-dependent form of the equations, in which the transmission term is given by The most basic epidemic model is the ODE, compartmental SIR model . The threshold of In epidemic models, the basic reproduction number acts as a threshold value that controls the local stability of the disease-free equilibrium: if mentclass2pt{minimeffective reproduction number, S(t)/N needs to be accounted for. When working with infectious disease interventions, we are often trying to get The approximations that we made to think through the intuition for  Boumans . Essentiherd protection  but will need to vaccinate almost everyone for very infectious disease, like measles (which requires upwards of 95% coverage). Vaccine hesitancy is a big concern to many people in public health, because diseases that were once virtually eradicated can regain a foothold when coverage dips too low, even locally. Population assortativity, which describes the situation where people are more likely to contact others with similar demographic characteristics , can result in lower-than-expected levels of herd protection because the well-mixed assumptions that underlie the above equations are violated . Then, the disease-free equilibrium is the point in state space where all individuals are susceptible, denoted i, let i. Let i minus the rate of transfer into compartment i. ThenF and V be the Jacobian matrix  of F and V can be used to succinctly write the equations for the infected compartments in the ODE linearized at the disease-free equilibrium,Several methods exist for calculating the mentclass2pt{minimk of a disease-free population. The  entry of j during its lifetime, assuming that the population remains near the DFE and barring reinfection. The  entry of F is the rate at which infected individuals in compartment j produce new infections in compartment i. Hence, the  entry of the product i produced by the infected individual originally introduced into compartment k.To interpret the entries of The basic reproduction number is defined to be the spectral radius—that is, the magnitude of the largest eigenvalue—of the matrix K can be found by solving the characteristic equation I is the identity matrix the same size as K. An important feature of K is that all entries are nonnegative; this implies , then there are no nonnegative eigenvectors other than K is reducible, then there may be degenerate initial conditions).The matrix em Meyer ) that K’The next generation theorem tells us that our continuous ODE system is stable (no outbreak) if and only if M individuals across m infected compartments at the start of an epidemic, i.e., m infected compartments in the next generation, i.e., all of the individuals that were infected by the individuals in A for a disease system with two infectious compartments, say adults and children, respectively,A, i.e., the nth generation is Let x(t) is the vector of the number of people in each infected compartment over time.While it makes intuitive sense that we should expect an outbreak when the next generation of infected individuals is larger than the previous generation, it may be less obvious how this discrete process corresponds to our original continuous ODE model. To understand the connection, it is important to first remember that our goal is not to find an alternate, discrete approach to simulating the disease system. Instead, our goal is to determine the threshold that controls the stability of the ODE system near the disease-free equilibrium, that is, find the threshold that determines whether or not there will be an outbreak. To that end, let’s consider the linearized ODE disease system near the disease free-equilibrium. As we saw in Sect. t who were part of the nth generation. Then, x(t), the vector of the number of individuals in each of the infected compartments over time, regardless of generation, is the sum over all of the generations, n, irrespective of when they were infected. It is calculated as the cumulative number of people in the nth generation entering each infected class, namely Now, let’s distinguish between each generation in our ODE model, beginning with the initial generation K is not a substitute for the ODE system; instead the next generation matrix should be seen as an interpretable object that tells us whether we expect an outbreak for a given disease system.It is important to emphasize that this discussion is in the context of the linearized ODE system near the disease-free equilibrium. Because nonlinear ODE epidemic models quickly leave this regime, we should not expect the behavior of those trajectories to match these iterations of the next generation matrix. Hence, applying the next generation matrix K is the key threshold for the stability of these epidemic models.Although we now understand how using the next generation matrix as a linear process relates to the dynamics of our disease system, we still need to answer the question of why the spectral radius of K is the magnitude of the largest eigenvalue of K. To understand why the spectral radius is important, let us consider a geometric interpretation of an eigenvalue K. Recall that an eigenvalue A defined in Eq.  with radius M, i.e., the set of all vectors M. Although most of us are most familiar with the M has a diamond shape in M. In the Why are these eigenvalues and eigenvectors important? Let’s take a step back and consider the space of all possible initial generations M individuals partitioned into m infected compartments, K by matrix multiplication to a vector K to every vector in M, the size of each A given in Eq.  and  by the same amount, K is called the operator norm of K and is denoted Now, we have the set of all possible initial generation vectors mentclass2pt{minimK to the set of initial generation vectors n times. This process calculates the size of the nth generation of infected people and the distribution of the individuals among the m infected classes. The original shape becomes more exaggerated as the size of the subsequent generations increases. Iterations of this process tell us about the long-term behavior of the system linearized at the disease-free equilibrium for each possible initial condition  by a factor of K gives the expected size of an average next generation and why this basic reproduction number determines the stability of our ODE disease system near the disease-free equilibrium.How many new infections are in a new generation when we are near this stable distribution represented by mentclass2pt{minim(K) Fig. , using tA given in Eq.  and A to subsequent generations of infected individuals, these generations will converge toward a stable distribution of adults and children infected in each next generation. By normalizing the eigenvector corresponding to the largest eigenvalue, we can see that the distribution of new infections converges to 2/3 adults and 1/3 children. Regardless of initial distribution, the system moves to a stable distribution of new infections of both types, with an average of 2 new infections per infected individual overall.Let’s visualize the geometric interpretation of the next generation matrix in a few concrete examples. First, let’s revisit the next generation matrix n in Eq. . BecauseA. Note that although we show the transformation on the full unit circle, we are primarily interested in the behavior in the first quadrant, In Fig. mentclass2pt{minimmentclass2pt{minimmentclass2pt{minimB has eigenvalues 1.6 and 0.7, and we expect an outbreak. Matrix C, has eigenvalues 0.8 and 0.7, so, despite the fact that infections of type 2 produce an average of C is not irreducible because infections of type 1 cannot make infections of type 2, which is why C can have two nonnegative eigenvectors. But, one of these eigenvectors corresponds to a degenerate initial condition . All other initial conditions converge to the eigenvector associated with B and C.We repeat this exercise with two more matrices:One challenge in working with basic reproduction numbers calculated from the next generation method—particularly when using symbolic calculators—is interpreting and developing intuition for the form of Here, we explore the calculation and interpretation of entclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaL, which tracks the number of people with latent infections, who are not yet infectious . A second extension is to include vital dynamics, i.e., birth and death, here represented by the parameter L and I. We writeF and V with respect to L and I and evaluate them at the disease-free equilibrium, where ith component of the diagonal of V is rate of leaving compartment i . The other elements in the corresponding column are the rates of movement to the other infected compartments. The sum of a column gives the rate of becoming not infected (whether through recovery or death). Then,I given a person in either L or I near the disease-free equilibrium. Any new infections produce latent people, not infectious people directly.One simple extension of the SIR model is the SLIR model, in which we add a compartment The next generation matrix has two eigenvalues, 0 andWhen modeling many diseases, we often consider direct, person-to-person transmission, captured by the familiar S, I, and R compartments. We additionally track the concentration of pathogens in the water system W. In this model, people become infectious not through contact with one another but by drinking the water. People drink people getting infected, we do not treat pathogens shedding into the water . Each class of host only transmits the disease to the other class and not directly to other members of their own class .Here, we use a very simple model with two classes of individuals (1 and 2), each of which can be S, I, or R. Infectious members of each class transmit only to the other class with rates K, we can seeTo start with, let’s continue with the vectorborne example, and writeK. Indeed, the reader might like to explore how the formula for But what if we care about new infections in both classes? We have to rewriteDespite having different interpretations, both of the F and Because there is not always a single valid interpretation, it is worth asking how we know whether a choice to include a term in Watmough , but a dIn this final example, we address a common challenge that arises in interpreting I represents untreated infected individuals, and T represents treated infected individuals. We can think of this as a simplified model of treatment compliance. Here, treatment might reduce infectiousness and reduce the mortality rate. Parameters V, namely For the purposes of illustration, we use a very simple model with two classes of infected people with bidirectional movement. Here, X, a random number, be the number of visits to compartment I, and let Z be the event of a return visit to compartment I. Using the law of total expectationE[X], we getV can be similarly understood.Consider an individual with an untreated infection. They can either die with probability A be the matrix whose entries j to compartment i; this is the adjacency matrix of the directed graph of the compartments, weighted by transition probability . Theni if one starts in compartment j. Thus, we can write There is a graph-theoretic way of approaching this interpretation as well (Brouwer et al. The basic reproduction number


This is a retrospective report of the frequency of severe hypoglycemia and the association between common mental disorders and type 1 diabetes mellitus treated with insulin analogues. Patients with severe hypoglycemia compared with those without this complication had a higher prevalence of positive screening for common mental disorders . Intensive treatment of type 1 diabetes (T1D) prevents and slows the progression of long-term complications of the disease, but severe hypoglycemia is a barrier in achieving strict glucose control in these patients  and incrThe purpose of this study was to evaluate the frequency of severe hypoglycemia and its association with common mental disorders in patients with T1D treated with insulin analogues after introduction of these types of insulin in the public health system in Southern Brazil.To evaluate the aspects highlighted above, we studied 516 adults with T1D living across 38 cities in Southern Brazil. The patients included in this study were selected from participants in a program of no-cost distribution of insulin analogues, whose enrollment in Brazil requires at least two severe hypoglycemic events within a period of 6 months. Severe hypoglycemia, defined as hypoglycemic episodes requiring assistance from another person, was evaluated using a self-report questionnaire. The eligible population included patients aged 18 years or older who were using short-acting insulin analogues  and/or long-acting insulin analogues . Patients with cognitive deficits or communication barriers were excluded.Treatment satisfaction was evaluated using the Diabetes Treatment Satisfaction Questionnaire status version (DTSQs). The total DTSQs score varied from 0 to 36, with higher scores indicating greater treatment satisfaction , previously validated in the Brazilian population . The queHospital de Clínicas de Porto Alegre (Certificado de Apresentação para Apreciação Ética [CAAE] 1.283.728).The protocol of the present study was approved by the Research Ethics Committee of vs. 77%, respectively, p = 0.027), as well as more symptoms of depression, anxiety, somatic signs, and social withdrawal. Additionally, the median DTSQs score was lower in patients with severe hypoglycemia compared with those without this complication.The patients had a median age of 35 years (interquartile range 28-45 years) and were 52% women. In all, 101 (20%) patients reported severe hypoglycemia in the month before the data collection . All patThis study has some limitations. First, severe hypoglycemia is associated with depression , and sin


The aim of this study was to evaluate the frequency of hypoglycemia and the treatment satisfaction in patients with type 1 diabetes (T1D) using insulin analogues.This observational retrospective study included 516 adult patients with T1D from 38 cities in Southern Brazil. Demographics and clinical data were collected using a self-report questionnaire. Hypoglycemia was defined as an event based on either symptoms or self-monitored blood glucose < 70 mg/dL. Treatment satisfaction was evaluated using the Diabetes Treatment Satisfaction Questionnaire status version (DTSQs) and with a specific question with scores ranging from 0–10. Common mental disorders were assessed using the General Health Questionnaire (GHQ-12).Overall, the mean age was 38 ± 14 years and 52% of the participants were women. The median diabetes duration was 18 years. The scores for insulin analogue treatment satisfaction were higher than those for previous treatments. DTSQ scores had a median value of 32 (interquartile range 29–35) and remained unchanged over time. The percentage of patients with hypoglycemia  was comparable across groups divided according to duration of use of insulin analogues. Most patients  screened positive for common mental disorders.Patient satisfaction with insulin analogue treatment was high and remained unchanged with time. Episodes of hypoglycemia also remained unchanged over time among patients using insulin analogues. Type 1 diabetes mellitus is a chronic and progressive disease with an increasing incidence over the past decades. Estimates project that 5–10% of all 12 million patients with diabetes in Brazil have type 1 diabetes .The Diabetes Control and Complications Trial (DCCT) has shown that strict glycemic control in patients with diabetes significantly decreases the risk of chronic complications . After tBecause of their pharmacological profile, insulin analogues can better mimic endogenous insulin production compared with human insulin, thus contributing to a decreased frequency of hypoglycemia and improved treatment satisfaction . Some stAnother limitation in interpreting the results of studies that have associated the use of insulin analogues with better patient satisfaction with treatment is that other parameters such as depression and anxiety can also impact patient satisfaction . Indeed,In addition to the concerns regarding the potential advantages of therapy with insulin analogues, it is unclear whether patient satisfaction with this specific therapy could reduce over time, as observed with other interventions in chronic diseases . We hypoHospital de Clínicas de Porto Alegre (Certificado de Apresentação para Apreciação Ética –CAAE-1.283.728).This was an observational study carried out from April 2016 to December 2017. The protocol of the study was approved by the research ethics committee of The primary outcomes were the frequency of hypoglycemia and treatment satisfaction of patients with type 1 diabetes mellitus after starting treatment with insulin analogues provided by the government, seeking possible predictors of greater satisfaction and fewer hypoglycemic events. A secondary outcome was the impact of the duration of treatment with insulin analogues on the primary outcomes.The public health system in the state of Rio Grande do Sul is geographically divided into 19 regional health coordinating units. These 19 units comprehend different numbers of municipalities, totaling 498 in the entire state. To determine the locations for data collection, we first selected 19 municipalities to represent each coordinating unit. Other 21 municipalities were randomly selected to complete the number of patients necessary to represent the regional health coordination units that received insulin analogues in the state.Patients were invited to participate in the study upon their arrival at the pharmacy to pick up short- or long-acting insulin analogues dispensed by administrative or judicial procedures. A written informed consent was signed by the patients included in the study or their legal guardians.The eligible population comprised patients aged 18 years or more, with type 1 diabetes mellitus treated with short-acting  and/or long-acting  insulin analogues supplied by the Health Secretariat of Rio Grande do Sul (SES-RS) via an administrative or judicial procedure. Patients with impaired cognition or communication barriers were excluded.i.e., type 1 diabetes mellitus, use of human insulin for at least 6 months, HbA1c level < 12%, at least two severe hypoglycemic events over 6 months, and follow-up with an endocrinologist for at least 6 months. Patients who did not meet all the criteria to receive insulin analogue via an administrative procedure appealed to the judiciary to receive the medication via a judicial procedure.To receive insulin analogues via an administrative procedure, a patient is required to have certain inclusion criteria, Clinical and sociodemographic characteristics were obtained using a self-report questionnaire. For the hypoglycemic outcomes, the questionnaire referred to the month before study enrollment. Hypoglycemia was defined as an event based on either symptoms or self-monitored blood glucose < 70 mg/dL. Severe hypoglycemia was defined according to the American Diabetes Association as any hypoglycemic event requiring assistance of another person to actively administer carbohydrate, glucagon, or other resuscitative actions . Nocturni.e., 0 represents “never”, and 6 represents “most of the time”. Scores for all DTSQs items except for items two and three were used for the total DTSQs score, which ranged from 0 to 36. Higher scores indicated greater treatment satisfaction.Treatment satisfaction was analyzed using the Portuguese version of the Diabetes Treatment Satisfaction Questionnaire status version (DTSQs), an 8-item inventory assessing therapy for type 1 diabetes mellitus in the previous weeks. This survey measures general satisfaction, adequacy, flexibility, diabetes understanding, and willingness to recommend the current treatment to other people and maintain such treatment . Each itFor mental health screening, the participants filled out the 12-item General Health Questionnaire (GHQ-12) translated into Brazilian Portuguese and validated for the Brazilian population . This seExperience with the current and previous treatments was also evaluated using a specific question with scores ranging from 0 to 10, obtained through a self-report questionnaire. Other relevant survey information, including the patients’ weight (kg), height (cm), and insulin dispensing process , were collected from the SES-RS computerized drug delivery system by a trained and authorized researcher.Considering an alpha error of 5% and a confidence interval range of 8%, the sample size calculation yielded 527 individuals.Data were described using measures of central tendency  and dispersion (standard deviations and interquartile range [IQR]) for continuous variables and absolute numbers and proportions for categorical variables. Analysis of variance (ANOVA) was used for comparing numerical variables with normal distribution, and the Kruskal-Wallis test for data without normal distribution. The Mann-Whitney and Wilcoxon tests were used for paired samples, and the chi-square test was used to compare categorical variables. P values < 0.05 were considered statistically significant. All analyses were performed using the statistical software SPSS v.16.0 .From 4124 patients with type 1 diabetes mellitus older than 18 years receiving insulin analogues from SES-RS, we selected 566 potential participants. Of these, 50 were excluded for not meeting the inclusion criteria, yielding a final sample of 516 patients. The patients in the final sample belonged to 38 of the 40 municipalities included in the sample selection. The collection of data from some municipalities was difficult and we were unable to achieve 100% representation for each coordinating unit.2  was 25 ± 5 kg/m2 . The med2 . RegardiAmong all patients included in the study, 350 (74%) reported hypoglycemia the month before data collection. Regarding the type of hypoglycemia experienced, 113 patients (22%) described severe hypoglycemia and 211 (41%) reported nocturnal hypoglycemia. A total of 144 patients (28%) reported more than four hypoglycemic events in the previous month. There were no differences among the percentages of patients reporting hypoglycemia when analyzed by groups divided according to duration of use of insulin analogues  or type The median DTSQs score in the overall sample was 32 (IQR 29–35) . The medThe satisfaction score with the experience with current treatments  was obtained with a specific question with scores ranging from 0 to 10. The score obtained  was higher than the score obtained for previous treatments (NPH and/or regular human insulin), which was 5 , reflecting increased satisfaction over time (p < 0.001). Patients without prior use of NPH or regular human insulin were not included in these analyses.Most patients  had a positive screening  for common mental disorders . The ratStrict glucose control in type 1 diabetes mellitus reduces the risk of chronic disease complications at the expense of an increased number of hypoglycemic events ,18. The Hypoglycemia is one of the main problems in patients with type 1 diabetes mellitus. These patients have an indeterminable number of episodes of asymptomatic hypoglycemia and an average of two events of symptomatic hypoglycemia per week . In the An interesting finding of our study was that the rate of hypoglycemia remained unchanged over time in patients who already used insulin analogues. In contrast, data from the literature indicate that the risk of hypoglycemia increases markedly with the duration of the disease .As a group with prior risk of severe hypoglycemia, our study patients who received insulin analogues via administrative proceedings abided to the requirement of the dispensing protocol, in which the presence of at least two events of severe hypoglycemia within 6 months was one of the criteria. These patients receive insulin free of charge from the state, but bear the expense of the needles that are necessary for insulin injection, thereby promoting, in many cases, excessive needle reuse .Similar to previously described in other self-report studies, the patients in the present study identified the episodes of hypoglycemia by the occurrence of symptoms, blood glucose testing alone, or a combination of both ,22. ThisAnother important finding of our study was that the patients were considerably more satisfied with the current diabetes treatment with insulin analogues compared with prior treatment with NPH and regular human insulin, as evaluated using a specific question with scores ranging from 0 to 10. This may be an important result since, according to the literature, treatment satisfaction is associated with better glucose control . PreviouIn our evaluation of the DTSQs per individual item, satisfaction was high in the six subscales of the questionnaire. Factors such as the similarity of insulin analogues to endogenous insulin with regard to pharmacokinetics, regimen complexity reduction, and dose frequency may have contributed to greater treatment satisfaction among our patients ,29. The Our study found a high rate of patients screening positive for common mental disorders (77%) compared with a previous study in 358 patients with diabetes, in which 29% screened positive for common mental disorders . HoweverOn analysis stratified by duration of use of insulin analogues, patients using insulin analogues for less than 1 year were older than those using insulin analogues for more than 5 years. This finding of patients newly enrolled in the protocol for the acquirement of insulin analogues being older at diagnosis may be related to a more stringent technical criteria for access to insulin analogues implemented after updates in the SES-RS dispensing protocol.e.g., HbA1c), which were not available in the SES-RS computerized drug delivery system. Also, we did not evaluate if hypoglycemia unawareness could have influenced our results. However, the characteristics of our study enabled the inclusion of more patients, driving significant observations in regard to the real-life impact of patient satisfaction with treatment. The sample represented more than 50% of most regional health coordinating units, and due to factors including temporary shortages of insulin analogues and difficulty of applying the questionnaire in some cities, did not include the entire planned sample but was overall representative of the population.Limitations of the present study include the fact that patients who are dissatisfied with the therapy or with the paperwork involved for acquiring insulin analogues in Brazil have the option to switch back to human insulin, which is also offered free of charge. Other limitations include the observational and retrospective design of the study, and the use of self-report data (which may not have reflected accurately the current blood glucose level of the participants in cases of hypoglycemia), and the absence of measurements of glycemic control (In conclusion, despite high rates of hypoglycemia and positive screening for common mental disorders, patients with type 1 diabetes mellitus using insulin analogues maintained great satisfaction with their diabetes treatment, which remained unchanged in the long term, unlike reports from other interventions in chronic diseases. Due to our study design, we were unable to confirm that the hypoglycemia rates were higher with treatment received prior to insulin analogues, as hypothesized. Observational studies, such as the present one, are essential since they are conducted in a real-world environment and provide valuable data regarding the use of a drug in clinical practice without the strict supervision of a randomized controlled trial. Additional studies and analyses are needed to investigate in each health macroregion of the state a potential association between hypoglycemia and conventional predictive factors of hypoglycemia including ethnic, cultural, and health-related factors.