However, due to the challenge of implementing reconfigurability, deploying different DNNs algorithms requires re-building and duplicating the physical diffractive systems, which significantly degrades the hardware efficiency in practical application scenarios. Thus, this work proposes a novel hardware-software co-design method that enables first-of-its-like real-time multi-task learning in D22NNs that automatically recognizes which task is being deployed in real-time. Our experimental results demonstrate significant improvements in versatility, hardware efficiency, and also demonstrate and quantify the robustness of proposed multi-task D2NN architecture under wide noise ranges of all system components. In addition, we propose a domain-specific regularization algorithm for training the proposed multi-task architecture, which can be used to flexibly adjust the desired performance for each task.Deep neural networks (DNNs) have substantial computational requirements, which greatly limit their performance in resource-constrained environments. Recently, there are increasing efforts on optical neural networks and optical computing based DNNs hardware, which bring significant advantages for deep learning systems in terms of their power efficiency, parallelism and computational speed. Among them, free-space diffractive deep neural networks (D DNNs have also impacted practical technologies such as web search, autonomous vehicles, and financial analysis1. However, DNNs have substantial computational and memory requirements, which greatly limit their training and deployment in resource-constrained  environments. To address these challenges, there has been a significant trend in building high-performance DNNs hardware platforms. While there has been significant progress in advancing customized silicon DNN hardware (ASICs and FPGAs)4 to improve computational throughput, scalability, and efficiency, their performance (speed and energy efficiency) are fundamentally limited by the underlying electronic components. Even with the recent progress of integrated analog signal processors in accelerating DNNs systems which focus on accelerating matrix multiplication, such as Vector Matrix Multiplying module (VMM)5, mixed-mode Multiplying-Accumulating unit (MAC)8, resistive random access memory (RRAM) based MAC13, etc., the parallelization are still highly limited. Moreover, they are plagued by the same limitations of electronic components, with additional challenges in the manufacturing and implementation due to issues with device variability12.The past half-decade has seen unprecedented growth in machine learning with deep neural networks (DNNs). Use of DNNs represents the state-of-the-art in many applications, including large-scale computer vision, natural language processing, and data mining tasks23. Among them, free-space diffractive deep neural networks (D2NNs) , which is based on the light diffraction, feature millions of neurons in each layer interconnected with neurons in neighboring layers. This ultrahigh density and parallelism make this system possess fast and high throughput computing capability. Note that the diffractive propagations controlled by such physical parameters are differentiable, which means that such parameters can be optimized via conventional backpropagation algorithms19 using autograd mechanism24.Recently, there are increasing efforts on optical neural networks and optical computing based DNNs hardware, which bring significant advantages for machine learning systems in terms of their power efficiency, parallelism and computational speed2NNs is that such a platform can be scaled up to millions of artificial neurons. In contrast, the design and DNNs deployment complexity on other optical architectures, e.g., integrated nantophotnics25 and silicon photnics23), can dramatically increase. For example, Lin et al.16 experimentally demonstrated various complex functions with an all-optical D2NNs. In conventional DNNs, forward prorogation are computed by generating the feature representation with floating-point weights associated with each neural layer. In D2NNs, such floating-point weights are encoded in the phase of each neuron of diffractive phase masks, which is acquired by and multiplied onto the light wavefunction as it propagates through the neuron. Similar to conventional DNNs, the final output class is predicted based on generating labels according to a given one-hot representation, e.g., the max operation over the output signals of the last diffractive layer observed by detectors. Recently, D2NNs have been further optimized with advanced training algorithms, architectures, and energy efficiency aware training26, e.g, class-specific differential detector mechanism improves the testing accuracy by 1–3%26 improves the robustness of D2NNs inference with data augmentation in training.In terms of hardware performance/complexity, one of the significant advantages of D2NNs , deploying a different DNNs algorithm requires re-building the entire D2NNs system. In this manner, the hardware efficiency can be significantly degraded for multiple DNNs tasks, especially when those tasks are different but related. This has also been an important trend in conventional DNNs, which minimizes the total number of neurons and computations used for multiple related tasks to improve hardware efficiency, namely multi-task learning27. Note that, realizing different tasks directly from the input data features without separate inputs or user indications is challenging even in conventional DNNs system. In this work, we present the first-of-its-kind real-time multi-task D2NNs architecture optimized in hardware-software co-design fashion, which enables sharing partial feature representations  for multiple related prediction tasks. More importantly, our system can automatically recognize which task is being deployed and generate corresponding predictions in real-time fashion, without any external inputs in addition to the input images. Moreover, we demonstrate that the proposed hardware-software co-design approach is able to significantly reduce the complexity of the hardware by further reusing the detectors and maintain the robustness under multiple system noises. Finally, we propose an efficient domain-specific regularization algorithm for training multi-task D2NNs, which offers flexible control to balance the prediction accuracy of each task (task accuracy trade-off) and prevent over-fitting. The experimental results demonstrate that our multi-task D2NNs system can achieve the same accuracy for both tasks compared to the original D2NNs, with more than 75% improvements in hardware efficiency; and the proposed architecture is practically noise resilient under detector Gaussian noise and fabrication variations, where prediction performance degrades ≤ 1% within the practical noise ranges.However, due to the challenge of implementing reconfigurability in D2NN) architecture. Specifically, in this work, our multi-task D2NN deploy image classification DNN algorithms with two tasks, i.e., classifying MNIST10 dataset and classifying Fashion-MNIST10 dataset. In a single-task D2NN architecture for classification16, the number of opto-electronic detectors positioned at the output of the system has to be equal to the number of classes in the target dataset. The predicted classes are generated similarly as conventional DNNs by selecting the index of the highest probability of the outputs (argmax), i.e., the highest energy value observed by detectors. Moreover, due to the lack of flexibility and reconfigurability of the D2NN layers, deploying DNNs algorithms for N tasks requires physically designing N D2NN systems, which means N times of the D2NN layer fabrications and the use of detectors. Our main goal is to improve the cost efficiency of hardware systems while deploying multiple related ML tasks. Conceptually, the methodologies behind multi-task D2NN architecture and conventional multi-task DNNs are the same, i.e., maximizing the shared knowledge or feature representations in the network between the related tasks27.Figure 2NN multi-task learning problem over an input space N is the number of tasks and D is the size of the dataset for each task. The hypothesis for D2NN multi-task learning remains the same as conventional DNNs, which generally yields the following empirical minimization formulation:2NN will deploy the mapping, shared diffractive layers between tasks and task-specific parameters multi-task diffractive layers. Specifically, in this work, we design and demonstrate the multi-task D2NN with a two-task D2NN architecture shown in Figure 16. The output C detectors, where C is the largest number of classes among all tasks; for example, 2NN system is constructed by designing six phase modulators based on the optimized phase parameters in the four shared and two multi-task layers . The design of phase modulators can be done with 3D printing or lithography to form a passive optical network that performs inference as the input light diffracts from the input plane to the output. Alternatively, such diffractive layer models can also be implemented with spatial light modulators (SLMs), which offers the flexibility of reconfiguring the layers with the cost of limiting the throughput and increase of power consumption.Let the Dmentclass2pt{minimmentclass2pt{minim2NN architecture, which requires two stand-alone D2NN systems; (2) multi-task D2NN architecture with the same diffractive architecture as Fig. Accuracy-Hardware product (a.k.a. Acc-HW) metric. Regarding the hardware cost, we estimate the cost of the baseline and the proposed systems using the number of detectors. This is because the major cost of the system comes from detectors in practice and the cost of 3D-printed masks is negligible compared to detector cost. To evaluate the hardware efficiency improvements, we set single-task Acc-HW as the baseline, and the improvements of the multi-task D2NN architectures using Eq.  using only 10 detectors; and gains over 55% and 50% compared to using an architecture that requires two separate sets of detectors.Table sing Eq. . We can 2NN evaluated in this work, both MNIST and Fashion-MNIST have ten classes. Thus, all the detectors used for one class can be fully re-utilized for the other. To enable an efficient training process, we use one-hot encodings for representing the classes similarly as the conventional multi-class classification ML models. The novel modeling introduced in this work that enables re-using the detectors is—defining “1” differently in the one-hot representations. As shown in Fig. det)—the index of the lowest energy area. Similarly, Fig. det). Therefore, ten detectors can be used to generate the final outputs for two different tasks that share the same number of classes, to gain extra 55% and 50% hardware efficiency of the proposed multi-task D2NN , and the y-axis shows the accuracy. Figure While building conventional multi-task DNN, it is well known that the robustness of the multi-task DNNs degrades compared to single-task DNNs, for each individual task. Such concerns become more critical in the proposed multi-task Dods Eqs. –10. FiguL2 normalization applied on multi-task layers only. The results with 100 trials of training  are included in Fig. 2NN architecture, regardless of the initialization and training setups. Moreover, Fig. In multi-task learning, it is often needed to adjust the weight or importance of different prediction tasks according to the application scenarios. For example, one task could be required to have the highest possible prediction performance while the performance of other tasks are secondary. To enable such biased multi-task learning, the shared representations 2NN architecture. Based on the phase parameters 2D2NN system. For example, the passive diffractive layers can be manufactured using 3D printing for long-wavelength light (e.g. terahertz) or lithography for short-wavelength light (e.g. near-infrared), and active reconfigurable ones can be implemented using spatial light modulators. A 50–50 beam splitter is used to split the output beam from the last shared diffractive layer into two ideally identical channels for multi-task layers. Coherent light source, such as laser diodes, is use in this system. At the output of two multi-task layers, the electromagnetic vector fields are added together on the detector plane. The generated photocurrent corresponding to the optical intensity of summed vector fields is measured and observed as output labels. Regarding the real-time capability of the proposed system, the proposed architecture performs the same the system proposed in16, where computation is executed at the speed of light and the information is processed on each neuron/pixel of the phase mask is highly parallel. Thus, the time of light flight is negligible and the determination factor for system hardware performance is dependent on the performance of THz detectors. For a detector with operation bandwidth f, the corresponding latency is 1/f and the largest throughput is f frames/s/task. The minimum power requirement for this system is determined by the number of detector, NEP , and , if we assume the loss and energy consumption associated with phase masks is negligible. In practice, considering a room-temperature VDI detector (https://www.vadiodes.com/en/products/detectors?id=214) operating at ps, throughput is uW. In addition to mitigate the large cost of detectors, alternative materials can be used, such as graphene. For example, the specific detector performance shown in28 is uW.Figure 2NN PyTorch implementation includes (1) diffractive layer initialization and forward function, (2) beam splitter forward function, (3) detector reading, and (4) final predicted class calculation. First, each layer is composed of one diffractive layer that performs the same phase modulation as16. To enable high-performance training and inference on GPU core, we utilize for complex-to-complex Discrete Fourier Transform in PyTorch (torch.fft) and its inversion (torch.ifft) to mathematically model the same modulation process as16. Beam splitter that evenly splits the light into transmitted light and reflected light is modeled as dividing the complex tensor produced by the shared layers in half. The trainable parameters are the phase parameters in the diffractive layers that modulate the incoming light. While all the forward function components are differentiable, the phase parameters can be simply optimized using automatic differentiation gradient mechanism (autograd). The detector has ten regions and each detector returns the sum of all the pixels observed  and Pytorch (v1.6.0). The basic components in the multi-task Dc for task 1 (MNIST), minimize the value observed at (c+1)th detector, as well as maximize the values observed at other detectors; (2) given an input image in class c for task 1 (Fashion-MNIST), maximize the value observed at (c+1)th detector, as well as minimize the values observed at other detectors. Thus, the resulting multi-task model is able to automatically differentiate which task the input image belongs to based on the sum of values observed in the ten detectors, and then generate the predicted class using argmin (argmax) function for MNIST (Fashion-MNIST) task. The gradient updates have been summerized in Eq. (The original labels d in Eq. .7\documeith task with detector noise is shown in Eq. (We demonstrate that the proposed system is robust under the noise impacts from the device variations of diffractive layers and the detector noise in our system. Specifically, to include the noise attached to the detector, we generate a Gaussian noise mask n in Eq. .8\documeWe also considered the imperfection of the devices used in the system. With 3D printing or lithography based techniques, the imperfection devices might not implement exactly the phase parameters optimized by the training process. Specifically, we consider the imperfection of the devices that affect the phases randomly under a Gaussian noise. As shown in Fig. entclass1pt{minimaFinally, for results shown in Fig. Supplementary Information.


Acute kidney injury (AKI) is associated with increased mortality in most critical settings. However, it is unclear whether its mild form (i.e. AKI stage 1) is associated with increased mortality also in non-critical settings. Here we conducted an international study in patients hospitalized with SARS-CoV-2 infection aiming 1. to estimate the incidence of AKI at each stage and its impact on mortality 2. to identify AKI risk factors at admission (susceptibility) and during hospitalization (exposures) and factors contributing to AKI-associated mortality. We included 939 patients from medical departments in Moscow (Russia) and Padua . In-hospital AKI onset was identified in 140 (14.9%) patients, mainly with stage 1 (65%). Mortality was remarkably higher in patients with AKI compared to those without AKI . Such association remained significant after adjustment for other clinical conditions at admission  or restricting to AKI stage 1  or to subjects with AKI onset preceding deterioration of clinical conditions. After hospital admission, worsening of hypoxic damage, inflammation, hyperglycemia, and coagulopathy were identified as hospital-acquired risk factors predicting AKI onset. Following AKI onset, the AKI-associated worsening of respiratory function was identified as the main contributor to AKI-induced increase in mortality risk. In conclusion, AKI is a common complication of Sars-CoV2 infection in non-intensive care settings where it markedly increases mortality risk also at stage 1. The identification of hospital-acquired risk factors and exposures might help prevention of AKI onset and of its complications. Furthermore, acute kidney injury (AKI) has been reported to worsen the prognosis, being the most frequent condition reported in patients deceased with Sars-CoV-2 infection beyond respiratory failure4. The prevalence of AKI in COVID-19 studies is highly variable due to the heterogeneity of settings and the severity of the studied populations. AKI incidence exceeds 50% in intensive care unit (ICU) patients5, while in patients referred to non-intensive medical departments the rates are much lower (4 to 32%)4. A recent consensus document provides recommendations for the diagnosis, prevention, and management of COVID-19-associated AKI8. The majority of epidemiological studies report AKI rates and their association with mortality in critically-ill COVID-19 ICU patients focusing on severe AKI stages and their impact on outcomes. Information on less severe patients with mild AKI cases (stage I) admitted to non-ICU settings is lacking. Longitudinal studies evaluating exposures associated with the onset of AKI during hospitalization in COVID-19 patients as well as the impact of AKI at any stage on COVID-19 related mortality are also scant.More than a year after SARS-CoV-2 was identified, the world is still facing significant health challenges. Infected patients mostly present mild symptoms and rapid recovery. Nevertheless, besides older age, male gender, diabetes (DM), and cardiovascular disease (CVD), decreased eGFR represents the strongest independent predictor of poor outcomes This multicenter international study aims at describing the incidence of AKI (any stage) in patients admitted to non-ICU medical departments  with COVID-19-associated pneumonia, and the relevant impact on mortality. We describe clinical and biochemical risk factors at admission (susceptibility) and hospital-acquired risk factors for AKI (exposures), and identify the conditions contributing to AKI-associated mortality in COVID-19 patients.The study included 939 patients hospitalized with Sars-CoV-2 infection. AKI was recorded in 140 (14.9%) subjects and occurred mainly within the first four days of hospitalization ; 91 (65%), 22 (16%), and 27 (19%) patients had AKI stage 1, 2, and 3, respectively. As shown in Table Over a median observation of 14 days (IQR 9–17), 151 patients (16.1%) had a severe course (defined as admission to ICU or death), including 89 patients that died during the hospitalization (11.6%). In age and sex adjusted analyses, presence of diabetes, CKD, dyspnea, shorter duration between symptom onset and hospitalization, presence of gastrointestinal symptoms, and prior use of NSAID were associated with higher mortality , the onset of AKI increased the mortality rate significantly and independently from other confounding factors. Indeed subjects with both AKI and worsening of respiratory function and those with worsening of respiratory function without AKI, had an overall mortality of 54% and 32%, respectively .To evaluate factors associated with AKI onset during hospitalization we analyzed data on 362 patients admitted to the Padova center. As shown in Suppl. Table The onset of AKI after hospital admission was independently associated with a twofold increase in mortality . To identify the possible contributors of such AKI-induced increased mortality, we evaluated factors both worsening after AKI onset and being associated with higher mortality. AKI onset was followed by worsening of respiratory function (SaO2 and P/F), increases in markers of hypoxic damage (lactic acid) and cell death (LDH), higher glucose levels and WBC count, decrease in platelet count and worsening of coagulopathy (elongation on PT and APTT), liver-muscle damage (CPK and GGT), and, as expected, increase in creatinine, potassium levels, urine density and proteinuria Fig. . Of thesThis international multi-center study conveys four key important messages: (1) AKI is a common complication of COVID-19 also in patients hospitalized in non-intensive care units. (2) Beyond the established factors affecting AKI susceptibility , changes in inflammatory markers, glycemia, and indexes of coagulopathy and hypoxic damages represent hospital-acquired exposures increasing the risk of AKI. (3) AKI at any stage, including stage 1, is independently and strongly associated with mortality. (4) AKI-related worsening of respiratory function could be one of the the main driver of the AKI-induced increase in mortality risk.10. However, most studies were on severely ill patients, with a large proportion admitted in ICU, and none of them distinguished whether AKI preceded or followed admission to ICU. Our international study adds important information since it was conducted in medical departments, thus providing information on AKI onset in the non-ICU setting. In our cohort, the prevalence of AKI was 14.9% and, according to the KDIGO classification, 65% of patients had AKI stage 1. Considering those of previous studies demonstrating that even a slight serum creatinine increase  may result in medium-long term complications 13, this is an important observation with implications for health care systems.All these results highlight important and novel clinical insights. Previous studies have reported a highly variable prevalence of AKI among patients hospitalized for COVID-19 (from 4 to 40%)8. The onset of AKI during hospitalization can be considered as the results from an interplay between patient susceptibility (i.e. risk factors at admission) and many exposures during hospitalization . In this context, most studies on AKI have identified only risk factors at hospital admission 16, but no study aimed to identify possible hospital-acquired risk factors. Conversely, we provide novel information on factors acting as possible in-hospital exposures increasing the risk of AKI onset . Identification of these elements may guide KDIGO preventive/protective strategies and prioritize resource allocation in times of pandemic with high health care demand8. They might improve the identification of subjects at higher risk of AKI during hospitalization, and help physicians to monitor, anticipate, and possibly prevent AKI onset. Although the observational design of this study does not allow establishing any causal relationship, some of these factors might be causally associated with AKI onset. For example, inflammation and hyperglycemia are of particular interest, and further studies are recommended to evaluate whether might be considered as “actionable and modifiable factors” to prevent progression towards AKI.The risk factors for AKI onset in patients with COVID-19 pneumonia are poorly understood, as recently recognized by the consensus report of the 25th Acute Disease Quality Initiative Workgroup on COVID-19 associated AKI17. However, only a few studies evaluated the impact of AKI on mortality according to AKI stages, and none distinguished between AKI onset before ICU admission or whether AKI onset followed the deterioration of the clinical condition of patients. Again, our study provides detailed and novel clinical information. Indeed, it reports that the development of mild-to-severe AKI (any stage) was independently associated with markedly increased mortality in patients with Sars-Cov2 infection also in a non-intensive care setting. Most importantly, given the consistency of the results after exclusion of subjects developing AKI after deterioration of general clinical conditions, our study further supports the concept that patients with Sars-Cov2 infection and AKI, die because of AKI and not “simply” with AKI. Remarkably, while the mortality rate was the highest among those subjects developing AKI and having concomitant pre-existing conditions (e.g. CKD and/or CVD), the relative impact of AKI on mortality was even more clearly emerging among patients without CKD or CVD. In these subjects, where mortality was overall lower, AKI onset was associated with almost tenfold increase in mortality rate as compared to subjects without AKI.Acute kidney injury has already been recognized as a complication in patients hospitalized for severe COVID-19 infection associated with increased mortality, length of hospital stay, and healthcare costs19, and as a prognostic factor in COVID-1921. Therefore, this suggest that AKI onset could also have a direct effect of worsening of interstitial lung disease and therefore on mortality in patients with COVID-19 pneumonia. Such hypothesis will require further studies to be confirmed and to dissect the mechanisms involved .Finally, we identified factors both possibly influenced by AKI (i.e. with significant changes after AKI onset), and associated with higher mortality, as possible contributors of the AKI-associated higher mortality. These factors were worsening of respiratory function, hypoxic damage and cellular deaths (higher lactic acid and LDH), thrombocytopenia, coagulopathy (higher INR and APTT), and creatinine levels. However, we found the AKI-associated reduction in platelet count or increases in APTT and INR, did not affect the association between AKI and death. Therefore, these changes were more likely epiphenomena to AKI onset and not contributors to its effect on mortality. On the other hand, our data suggest that the AKI-induced increased mortality risk was mainly attributable to the worsening of respiratory function. In other words, our data showed that a worst respiratory function at admission was a risk factor for AKI onset, and that beyond such association, AKI onset antecede a further deterioration in the respiratory function that was found to explain about 26–43% of the AKI-induced increase in mortality. Is it important to highlight that the retrospective observational design of our study warrant caution before implying the causality of these association. However, in line with this, we observed that AKI was followed by a significant increase in LDH and that such increase was independently associated with higher mortality. While LDH is more likely to be a marker and not an effector or mediator of AKI-induced mortality , it has been previously reported as an important biomarker for the activity and severity of interstitial lung diseaseOverall, this study shows how AKI (at any stage) had an important impact on overall mortality in all subgroups of patients hospitalized with COVID-19. Indeed, AKI increased mortality among those patients without pre-existing concomitant conditions or among those with preserved respiratory function at admission, but also among those patients being admitted with severe clinical conditions. For instance, patients with low oxygen saturation or those that required in-hospital invasive or non-invasive ventilation had a much higher mortality if they also had AKI as compared to those who did not had AKI. Therefore, while all patients with low oxygen saturation at admission had “per se” a tendency to get in-hospital worsening of respiratory function and therefore of an higher COVID-19 mortality risk, those patients developing also AKI are those who should be considered at the highest mortality risk .Our study should be interpreted in the context of several limitations. First, real-world routinely accumulated clinical data can be affected by missing data, however, we applied extensive multiple imputation techniques to avoid excluding patients from the analyses. Second, this study combined patients hospitalized from different countries and with different demographic backgrounds, hence with different clinical characteristics. On one side this approach increases generalizability of our findings, but on the others sides this approach also introduces variability in measured and unmeasured factors across centers. While the measured differences were accounted for in the analyses, ideally, to further dissect the risk factors and outcomes of AKI, further studies with a more homogeneous population might be warranted. Third, the retrospective design did not allow the collection of ad-hoc important variables such as urinary outputs, changes in body weight, and more advanced serum and urinary inflammatory markers that are not routinely analyzed or recorded. We were unable to include medications as possible exposures, since detailed information with changes in treatments during hospitalization was not available. Therefore, it is important to recognize that other risk factors (e.g. exposures) might be present and future studies should consider them.In conclusion, AKI is a common complication of Sars-Cov-2 infection in medical departments (outside ICU), with a high prevalence of AKI stage 1. AKI at any stage is an independent risk factor for mortality in a non-intensive care setting. The worsening of respiratory function following AKI is a possible main contributor to AKI-induced increased in mortality. Early identification of hospital-acquired risk factors for AKI  might help developing new strategies to mitigate AKI onset and impact COVID-19 mortality, as well as long-term consequences of AKI in COVID-19 patients.The study was carried out collecting retrospective anonymized patient data from electronic medical records at participating centers. The protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki. In agreement with national regulation on retrospective studies, the protocol was notified to the local ethical committees , and the need for the patient's informed consent was waived. We retrieved data on all consecutive patients hospitalized during the first Sars-CoV-2 outbreak in Northern Italy , at the Medical Department of the University Hospital of Padova . We screened records of all patients admitted to the hospital with a positive PCR test for SARS-CoV-2 on upper or lower airway samples, excluding those initially admitted in ICU.22. Patients from two different hospitals were included. All consecutive patients hospitalized with COVID-19 between April 5th and May 5th 2020, at the Endocrinology Research Center, Moscow, Russia, and randomly chosen 339 of 1207 patients hospitalized with COVID-19 between April 13th and August 2nd 2020, from the Tareev Clinic of Internal Diseases, Sechenov University, Moscow, Russia. The COVID-19 was confirmed with a positive PCR test for SARS-CoV-2 and/or by typical CT findings. Overall, 293 patients had a positive PCR test while the remaining had COVID19 diagnosis based on clinical and CT findings 2. Vital signs  and information on symptoms of COVID-19 (collected upon admission): time from onset of symptoms to hospitalization; the presence of fever, cough, dyspnea, and gastrointestinal symptoms) were collected. Essential laboratory exams at admission included: fasting plasma glucose, HbA1c, serum creatinine to estimate glomerular filtration rate (eGFR) using the CKD-EPI equation, white blood cell count (WBC), Hematocrit, Platelet count, C-reactive protein (CRP), and D-dimer. The same parameters were also measured during the entire hospital stay, and the worst value was considered .The following parameters were collected and analyzed in all patients: age, sex, concomitant cardiovascular risk factors , diabetes, CKD, other comorbidities , presence or absence of COVID-19 related pneumonia or interstitial lung disease (ILD), ongoing therapies before hospitalization were evaluated. Community-acquired AKI was documented by increased creatinine values at admission compared to historical values or values recorded until 48 h before admission. Pre-existing CKD was evaluated according to medical diagnosis at admission and GFR estimation. Pre-existing diabetes and newly diagnosed diabetes was defined as previously described23 based on changes in creatinine levels criteria (urine volume criteria were not applied since such information were not available). Creatinine was evaluated every 48–72 h allowing identification of the worst AKI stage throughout the hospital stay.AKI stage was defined according to KDIGO criteriaWe evaluated the cumulative incidence of AKI throughout the hospitalization period and compared it with the primary outcome of the study that was 30-day in-hospital mortality.Development of AKI at least 48 h after admission and before admission to ICU was considered as a secondary outcome in the analyses aimed at identifying admission and hospital-acquired factors associated with the onset of AKI.To identify risk factors at admission (AKI susceptibility) or during hospitalization (AKI exposure) associated with the onset of AKI during hospitalization, we analyzed in detail a subgroup of patients admitted to the University Hospital of Padova with AKI onset at least 48 h after admission and before eventual ICU admission. For these patients, complete data on multiple laboratory variables was available throughout the hospitalization stay.We evaluated as risk factors at admission (susceptibility): age, sex, comorbidities , symptoms at admission, medication before hospitalization, blood laboratory results  and urine biochemical results . The changes from baseline in these parameters  to the worst value recorder during hospitalization and before AKI onset (up to the day before the AKI onset) were evaluated as possible Hospital-acquired risk factors for AKI onset (exposures).The same variables were tested as possible factors contributing to AKI-induced increase in mortality risk. Specifically, we evaluated the changes from the first value available from the day of AKI onset to the worst value recorder after AKI onset to identify those factors influenced by AKI and associated with death .Continuous variables are reported as mean ± standard deviation (S.D.) or median (inter-quartile range), while categorical variables have been reported with percentages.24, included sex, age, and clinical center as covariates (Model 1). To account for other confounding factors, the effect of AKI on mortality was tested in different multivariable-adjusted analyses with increasing complexity: including, on top of Model 1, those pre-existing conditions (Model 2) and parameters at admission (Model 3) associated both with AKI and mortality risk. The association between AKI and death was evaluated both as a dichotomous variable and as an ordinal categorical variable testing the contribution of each AKI stage as compared to subjects without AKI. The different effect of AKI on mortality according to baseline patient’s characteristics, was tested including an interaction term in the Model 1. We evaluated the association between AKI and severity of COVID-19 (defined by the admission to ICU or death) by robust-error-variance Poisson regression and between AKI and length of hospital stay (among survivors) employing linear regression (with same covariates as in Model 1). Poisson regression models were tested for overdispersion with analyses of goodness-of-fit. Sensitivity analyses were further conducted in a subgroup of subjects including only those with AKI onset before admission to ICU and at least three days before death. To account for the differences between subjects hospitalized in Italy and Russia  were not imputed. Outcome analyses were performed on each imputed dataset, and the pooled estimated effects are presented26.To test the multivariable adjusted models, a full dataset of variables was needed. Thus, missing data were handled using multiple imputation (MI). MI was performed, assuming a missing at random (MAR) pattern, with a fully conditional specification (FCS) algorithm2. Briefly, we evaluated whether the association between AKI and mortality was affected, and in which proportion, after including in the model the changes in the parameters affected by AKI-onset. Statistical analyses were conducted with a significance threshold of p < 0.05 and were done in SAS version 9.4 (TS1M4), GraphPad PRISM v8.3.0.Additional analyses were performed in the group of subjects with onset of AKI during stay in the non-ICU setting. To identify factors  associated with the onset of AKI, we used the same model previously described (Model 1). To test whether AKI onset was associated with changes during hospitalization in variables of interest, we performed linear regression analyses including age, sex, and value at baseline (or at AKI onset) as covariates. To allow a meaningful interpretation and comparison of the effect between different parameters the effects were rescaled to the Standard Deviation of each variable. Finally, we tested whether the association between AKI and mortality was partially mediated by factors both influenced by AKI and associated with higher mortality, as previously describedIn agreement with national regulations on retrospective studies, the protocol was notified to the local ethical committees , and the need for the patient's informed consent was waived.Supplementary Information.


The purpose of this study is to evaluate the role of major psychiatric illness on patient outcomes after total joint arthroplasty.Patients with a diagnosis of a major psychiatric disorder undergoing total joint arthroplasty were retrospectively matched one-to-one with a cohort without such a diagnosis. Major psychiatric disorder in the registry was identified by diagnosis of anxiety, mood, or a psychotic disorder. Primary outcome of interest included perioperative Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Secondary outcomes included EuroQol-5D, adverse events, length of stay, 30-day readmission, and discharge destination.Total number of patients were 1828. The total hip arthroplasty  and the total knee arthroplasty psychiatric group  had significantly lower pre-operative WOMAC scores. At 3 months, the total hip arthroplasty  and total knee arthroplasty psychiatric group  again had significantly lower 3-month post-operative WOMAC score compared to the control groups. However, outcomes at 1 year were difficult to interpret, as patients with major psychiatric conditions had an extremely high loss to follow-up. Compared to the control groups, the total hip arthroplasty and total knee arthroplasty psychiatric group had an increased length of stay by 1.43 days (p < 0.01) and 0.77 days, respectively (p = 0.05). Similarly, the psychiatric groups were discharged directly home less often  than the control groups.Patients with the diagnosis of a major psychiatric illness have an increased length of stay and are more likely to require a rehabilitation facility, compared to the control groups. Arguably, of utmost importance, there is a very high rate of loss to follow-up within the psychiatric groups. As such, we recommend these patients should be treated for their diagnosis prior to total joint arthroplasty. Furthermore, importance of clinical follow-up should be emphasized carefully. From 1999 to 2008, the rate of total knee arthroplasty (TKA) in the United States nearly doubled.8 Recent evidence suggests that pre-operative psychological comorbidities may negatively impact patient-reported outcomes. Furthermore, unrecognized or undertreated psychiatric conditions may be more common than previously thought, and can also result in poor outcomes after TJA.911 The prevalence of depression in the TJA patient population is approximately 25%, whereas the prevalence of overall psychiatric illness in orthopaedic inpatients may range from 19% to 86%.1214 Patients with end-stage arthritis may be at higher risk of developing mental illness compared to the normal population as they often experience chronic pain, tend be of older age, and have numerous medical comorbidities.15We are now beginning to understand that outcomes post-TJA are determined by a number of patient-specific factors that may not be directly related to the surgery itself, including pre-operative expectations and psychological health.Given the relatively high prevalence of psychiatric illness and related risk factors in the TJA population, there has been increased interest on understanding how these two conditions affect each other. Therefore, the purpose of this study is to evaluate the role of major psychiatric illness on outcomes after TJA using the data available in the Alberta Bone and Joint Health Institute (ABJHI) registry. We hypothesize that patients with major psychiatric illnesses will report inferior outcomes after TJA compared to those without any psychiatric illness.After institutional ethics approval was granted, we retrospectively collected data from the ABJHI database to identify patients who had undergone primary THA or TKA with a minimum 1 year follow-up between 2013 and 2018. Exclusion criteria included unicompartmental knee arthroplasties, hip resurfacings, simultaneous TJAs, revision surgeries, TJA for infection, and TJA for periprosthetic fracture.To calculate the sample size, we looked at detecting the mean difference of over five points on the primary outcome (WOMAC). In order to reach α = 0.05 and power of 0.8, a minimum of 143 patients per group  was required. To increase the power of the study and strengthen our study results, we expanded the sample size to 828 THA patients and 1000 TKA patients.From this cohort we identified our psychiatric group, which included patients that also had a formal diagnosis of a major psychiatric disorder (50% TKA and 50% THA). Major psychiatric disorder was identified in the ABJHI database by determining if the patient had been formally diagnosed with any of the following conditions: bipolar disorder, anxiety disorders, major personality disorder, chronic mental health diagnoses, psychosis, and moderate depressive episodes. The diagnosis of psychiatric illness was captured in the medical history of patient using Clinical Risk Group data (CRG) from 3 years prior to their surgery admission date. These patients were matched one-to-one with patients without such diagnosis. Patients were matched by surgery type, year of surgery, age, gender, and medical comorbidities.Due to the limitations of our database, the most reliable method of capturing and including comorbidities in our analysis was as an aggregate category versus individual diagnoses. An independent and blinded assessor completed matching for both groups.The primary outcome of interest included patient-specific, self-reported outcomes, which was measured using validated scoring questionnaire of Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Patient outcome was defined pre-operatively and post-operatively at 3 months and 1 year using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and EuroQol-5D (EQ-5D) scores, and total change in score was measured by finding the difference between the pre-operative score and 12-month post-operative score.16 The ABJHI database reports WOMAC as a range from 0 to 100  × 100/96, –Range of WOMAC All transformed score is from 0 (the worst) to 100 (the best), –Range of WOMAC All raw score is from 0 (the best) to 96 (the worst)) with zero indicating total disability and 100 indicating no disability. Therefore, the lower WOMAC score in our results represents the higher disability.The WOMAC score is a 24-question validated, joint-specific tool that evaluates three dimensions, including pain (5 questions), stiffness (2 questions) and physical function (17 questions).17 ABJHI uses a five-level EQ-5D (EQ-5D-5L) version which consists of the EQ-5D descriptive system and the EQ visual analogue scale (EQ VAS). The descriptive system defines health status in five dimensions: mobility, self-care, usual activities, pain/discomfort and anxiety/depression. Each dimension has five categories: no problems, slight problems, moderate problems, severe problems and extreme problems.18 The EQ VAS records the patient’s self-rated health on a vertical visual analogue scale, where the endpoints are labelled ‘The best health you can imagine’ and ‘The worst health you can imagine’. The VAS can be used as a quantitative measure of health outcome that reflects the patient’s own judgement.18 The ABJHI has copyright permission and authorization to use both WOMAC and EQ-5D. ABJHI is also registered with EuroQol group.Similarly, the EQ-5D is a valid and standardized instrument for quantifying overall health status.Secondary outcomes included early medical and mechanical adverse events, infections, length of stay, 30-day readmission rates and final discharge destination. All patient information was anonymous and de-identified.Descriptive statistical analysis was completed by comparing control and psychiatric groups for both THA and TKA cohorts. Mean and standard deviation for continuous variables, and the frequency and proportion for categorical variables, were calculated to characterize and compare both groups. Statistical comparison of patient characteristics was performed using two-tailed student t-test and chi-square to detect statistically significant differences at 95% confidence level to ensure both groups were comparable at baseline. Univariate and mixed-effects linear regression were used to examine changes in the EQ-D5 and WOMAC scores. Length of stay (LOS) was calculated as the cumulative number of days spent in hospital between both groups. Cumulative LOS was log transformed and multivariate linear regression analysis was performed comparing cumulative log-transformed LOS for both groups. Risk adjustment factors in the regression model included BMI, sex, and number of pre-surgery risk comorbidities. Statistical analysis was completed with significance defined as p < 0.05. All statistical analyses were performed using the programme STATA version 13  software.In total, 1828 patients were included in our analysis. In THA subset, we included 414 patients in each group (n = 828). These groups otherwise had similar baseline demographics . In the Regarding the follow-up rate, both THA and TKA cohorts saw minimal loss to follow-up at the 3-month period, but significant loss to follow-up at the 1-year period. For the THA WOMAC cohort, only one pair was lost to follow-up at 3 months, while 378 pairs were lost to follow-up at 12 months, indicating a 91% loss to follow-up rate (LFR). The THA EQ5D cohort demonstrated a similar trend with 32% LFR (58 out of 182 pairs) at 3 months and 87% LFR (159 out of 182 pairs) at 1 year. The TKA WOMAC cohort demonstrated one percent LFR (4 out of 500 pairs) at 3 months and 93% LFR (464 out of 500 pairs) at 1 year. The TKA EQ5D cohort demonstrated 1% LFR (2 out of 195 pairs) at 3 months and 90% LFR (174 out of 195 pairs) at 1 year.With regard to patient-specific, self-reported outcomes (WOMAC and EQ-5D), both the THA ; however, there were no other statistically significant differences in EQ5D between the groups at the 3-month period in either of the THA or TKA subsets. Due to the significant LFR at 1 year for both THA and TKA cohorts, we were unable to validate any outcome analysis for that time period.Regarding the rest of the secondary outcomes, there was a trend for increased blood transfusion requirements in the TKA psychiatric group compared to the control group . In addition, the psychiatric THA and TKA groups both had significantly increased LOS compared to their respective control groups. In the THA subset, the psychiatric group had an increased LOS by 1.43 days (p = 0.0028). In the TKA subset, the psychiatric group had an increased LOS by 0.77 days (p = 0.050). Discharge disposition in general was similar between groups, but a sensitivity analysis demonstrated that patients in both the psychiatric THA and TKA groups were discharged home significantly less often, and required a rehabilitation facility, than their respective control groups. In the THA subset, the psychiatric group was discharged home 86.9% of the time compared to the control group at 91.8% (p = 0.024). In the TKA subset, the psychiatric group was discharged home 87.6% of the time compared to the control group at 92% (p = 0.022). There were no other differences between the two group subsets regarding 30-day readmissions, infections, mechanical adverse events and medical adverse events  and 4.With regard to adverse events, in the THA subset, mechanical  and medical  adverse events were higher in control group compared to psychiatric group . In the The primary purpose of this study was to evaluate the role of major psychiatric illness on validated, patient-reported outcomes in TJA. Although the differences in our study groups for the reported scores did not reach the minimal clinically significant differences, psychiatric illness resulted in significantly inferior patient outcomes in the early perioperative period. However, because our sample size was large, the chance that trivial differences impacting our ‘statistical significance’ is low. In other words, the fragility of our p value with the larger sample size is low. There were no major statistical differences in outcomes at 1 year, but we were unable to validate these claims due to the high LFR. In fact, both TKA and THA cohorts had a greater than 90% LFR. This extremely high loss to follow-up highlights a major concern and the need to reinforce the importance of follow-up within patients diagnosed with a psychiatric illness.Both groups saw significant improvement in functional outcome scores post-operatively for both WOMAC and EQ-5D scores. Regarding our secondary outcomes, patients with psychiatric illness were more likely to have increased LOS and non-routine discharge from hospital. In addition, in the TKA subset with psychiatric illness, there was a trend for increased blood transfusion requirements. There were no other differences in adverse events in either the TKA or THA group.1923 Buller et al.22 in a large retrospective database study demonstrated that patients with a diagnosis of dementia, depression, and schizophrenia had a higher risk of suffering an adverse event post-operatively. In a large retrospective review of the Medicare database, Klement et al.20 similarly showed that the presence of a psychiatric disease increased the risk of medical adverse events at the 90-day time period and also increased the risk of periprosthetic fracture, infection, dislocation, and THA revision. Based on these studies, it is clear that patients with a concomitant diagnosis of psychiatric illness are a high-risk group; however, the degree to which these conditions effect clinical outcome are still quite contentious. This is largely due to a number of unique cofounders in this specific population. Psychiatric disease is a complex systemic illness that often leads to poor lifestyle choices, increased comorbidities, as well as stigma and physician bias associated with such a diagnosis.23Although the relationship between psychiatric disorders and clinical outcomes after TJA are yet to be fully elucidated, current literature on the topic largely tends to support our results that psychiatric disease can increase risk of adverse events and result in poor patient outcomes.24 The reasons for this are likely multifactorial, as patients with mental illness tend to display maladaptive behaviours during rehabilitation. Some of these behaviours include activity limitation, delayed mobilization, and greater dependence on walking aids.26 In addition, complex pain pathways may make patients with psychiatric illnesses more prone to greater pain perception and delaying mobilization.27 These patients are likely to benefit from multidisciplinary care or specialized psychological support. Tristiano et al.28 demonstrated that patients who received psychological support through formalized one-on-one programmes after TJA were less likely to be depressed or anxious and were discharged from hospital 1.2 days earlier. From an economic and clinical perspective, the downstream effects of a focused support programme could reduce the total cost of a joint replacement, improve patient outcomes and reduce the loss to follow-up rates.Our results on LOS and discharge after TJA also correlated well with recent literature. Numerous studies have shown that LOS and rate of non-routine discharge is increased in patients that have psychiatric illness.29 This trend is largely supported in the literature, as several studies have demonstrated that psychiatric illness can result in poor compliance and lack of appropriate follow-up.2931 Zhang and Ye31 similarly demonstrated a high loss to follow-up of patients with depression being treated in an outpatient setting with nearly 58% LFR at 12 weeks. The majority of the patients lost to follow-up in the aforementioned study were lost primarily after the first 2 weeks, with steady losses seen after that on a biweekly basis, as compared to our study where the majority of patients were lost to follow-up after the 3-month visit. The synergistic effect of poor perioperative outcomes in this population combined with the high loss to follow-up may predispose patients to greater risk of adverse events and poor long-term outcomes. The downstream economic and clinical effects of poor follow-up rate in this population require further attention. Pre-operative psychiatric counselling, a multidisciplinary approach, and access to more mental health support services is necessary to identify at-risk patients to ensure they have appropriate follow-up instructions.Our study demonstrated a high LFR among patients with major psychiatric illness undergoing TJA. Among patients in both THA and TKA cohorts, we saw greater than 90% LFR. To our knowledge, this is the highest LFR in the literature that has been identified among patients with major psychiatric illness. In a longitudinal multicentre RCT study assessing follow-up after inpatient versus outpatient psychiatric care, LFR was as high as 46% at 12 months.13 completed a prospective study to determine whether baseline mental health affected outcomes in patients with depression who were undergoing TJA. He found that patients with depression had significant improvements in outcome post-operatively, but these gains were modulated strongly by mental health at the time of the procedure.21 There is also likely a complex bidirectional relationship between chronic pain associated with arthritis and psychiatric disorders. Papakostidou et al.32 demonstrated that the rate of depression decreased significantly from 42% to 7% within a year of TJA. Similarly, in a Norwegian prescription database study, it was found that hypnotic, analgesic, and anxiolytic medication was significantly decreased after THA.33 By treating the mechanical manifestations of arthritis, we may be indirectly treating the psychological manifestations of chronic pain, resulting in a positive synergistic effect on patient outcomes.Psychiatric illness is a very broad and encompassing term for a number of heterogeneous conditions. Better metrics and outcomes are also needed to determine how active a particular psychiatric disorder may be at the time of surgery. Halawi et al.The greatest strength of our study was our ability to include a large cohort for each comparison to increase the validity of our results and reduce the fragility of our p value. To our knowledge, this is the largest matched cohort study comparing outcomes among patients with psychiatric illnesses undergoing TJA. However, our study was largely limited by the nature of registry data.Based on the ABJHI registry, we were unable to categorize results based on the specific type of psychiatric illness. Furthermore, the term psychiatric illness covers a number of heterogeneous conditions that also encompass a wide spectrum of severity. Registry data are well suited to assessing functional outcomes after joint replacement, but due to inherent limitation of retrospective studies, it is difficult to address selection bias. Selection bias by the treating surgeon could have led to selection of patients with less severe or lack of an acute psychiatric illness. Database studies are also restricted by the accuracy of their coding system. Diagnosis coding is a simple and effective way to categorize comorbidities and define study cohorts, but it cannot reflect the acuity or the accuracy of such a diagnosis, especially one as heterogeneous as psychiatric illness. Ideally, a prospective study that captures information on psychiatric illness duration, severity, and treatment would be beneficial. We hope to use the findings of this study to provide the foundation for a prospective study.Our results demonstrate that psychiatric illness can result in worse patient reported and clinical outcomes in the early perioperative period after TJA. Patients with psychiatric illnesses can be expected to gain significant improvements in outcome after TJA that are comparable to a baseline population without psychiatric illness. However, patients with psychiatric illnesses are at increased risk of delayed discharge and requirements for a rehabilitation facility prior to being sent home. A high loss to follow-up rate is a major concern in this population, as it may increase sequalae of early adverse events and result in poor long-term outcomes. Evaluation of the type of psychiatric illness, severity, and acuity of the condition should be taken into context. Patients with active or severe psychiatric illness undergoing joint replacement should be referred to their psychiatrist or family physician for optimization of their condition prior to surgery. Furthermore, there should be a greater emphasis on the importance of follow-up in the post-operative period.Click here for additional data file.Supplemental material, sj-pdf-1-smo-10.1177_20503121211012254 for Do psychiatric disorders affect patient reported outcomes and clinical outcomes post total hip and knee arthroplasty? by Sahil Kooner, Jeremy Kubik, Saboura Mahdavi, Sophie (Ghashang) Piroozfar, Hoa Khong, Kanwal Mohan, Eldridge Batuyong and Rajrishi Sharma in SAGE Open MedicineClick here for additional data file.Supplemental material, sj-pdf-2-smo-10.1177_20503121211012254 for Do psychiatric disorders affect patient reported outcomes and clinical outcomes post total hip and knee arthroplasty? by Sahil Kooner, Jeremy Kubik, Saboura Mahdavi, Sophie (Ghashang) Piroozfar, Hoa Khong, Kanwal Mohan, Eldridge Batuyong and Rajrishi Sharma in SAGE Open MedicineClick here for additional data file.Supplemental material, sj-pdf-3-smo-10.1177_20503121211012254 for Do psychiatric disorders affect patient reported outcomes and clinical outcomes post total hip and knee arthroplasty? by Sahil Kooner, Jeremy Kubik, Saboura Mahdavi, Sophie (Ghashang) Piroozfar, Hoa Khong, Kanwal Mohan, Eldridge Batuyong and Rajrishi Sharma in SAGE Open MedicineClick here for additional data file.Supplemental material, sj-pdf-4-smo-10.1177_20503121211012254 for Do psychiatric disorders affect patient reported outcomes and clinical outcomes post total hip and knee arthroplasty? by Sahil Kooner, Jeremy Kubik, Saboura Mahdavi, Sophie (Ghashang) Piroozfar, Hoa Khong, Kanwal Mohan, Eldridge Batuyong and Rajrishi Sharma in SAGE Open MedicineClick here for additional data file.Supplemental material, sj-pdf-5-smo-10.1177_20503121211012254 for Do psychiatric disorders affect patient reported outcomes and clinical outcomes post total hip and knee arthroplasty? by Sahil Kooner, Jeremy Kubik, Saboura Mahdavi, Sophie (Ghashang) Piroozfar, Hoa Khong, Kanwal Mohan, Eldridge Batuyong and Rajrishi Sharma in SAGE Open MedicineClick here for additional data file.Supplemental material, sj-pdf-6-smo-10.1177_20503121211012254 for Do psychiatric disorders affect patient reported outcomes and clinical outcomes post total hip and knee arthroplasty? by Sahil Kooner, Jeremy Kubik, Saboura Mahdavi, Sophie (Ghashang) Piroozfar, Hoa Khong, Kanwal Mohan, Eldridge Batuyong and Rajrishi Sharma in SAGE Open MedicineClick here for additional data file.Supplemental material, sj-pdf-7-smo-10.1177_20503121211012254 for Do psychiatric disorders affect patient reported outcomes and clinical outcomes post total hip and knee arthroplasty? by Sahil Kooner, Jeremy Kubik, Saboura Mahdavi, Sophie (Ghashang) Piroozfar, Hoa Khong, Kanwal Mohan, Eldridge Batuyong and Rajrishi Sharma in SAGE Open MedicineClick here for additional data file.Supplemental material, sj-pdf-8-smo-10.1177_20503121211012254 for Do psychiatric disorders affect patient reported outcomes and clinical outcomes post total hip and knee arthroplasty? by Sahil Kooner, Jeremy Kubik, Saboura Mahdavi, Sophie (Ghashang) Piroozfar, Hoa Khong, Kanwal Mohan, Eldridge Batuyong and Rajrishi Sharma in SAGE Open MedicineClick here for additional data file.Supplemental material, sj-pdf-9-smo-10.1177_20503121211012254 for Do psychiatric disorders affect patient reported outcomes and clinical outcomes post total hip and knee arthroplasty? by Sahil Kooner, Jeremy Kubik, Saboura Mahdavi, Sophie (Ghashang) Piroozfar, Hoa Khong, Kanwal Mohan, Eldridge Batuyong and Rajrishi Sharma in SAGE Open Medicine


The coronavirus disease (COVID-19) is the global public health challenge currently persisting at a grand scale. A method that meets the rapid quantitative detection of antibodies to assess the body’s immune response from natural COVID-19 illness or vaccines’ effects is urgently needed. In the present study, an attempt was made to integrate a newly designed spectrometer to the COVID-19 test strip procedure; this augmentation provides the quantitative capacity to a lateral flow immunoassay (LFIA). Optical interpretation of results by quantitative α index, rather than visual qualification, can be done quickly, in 5–10 minutes. The developed product was compared with several other serological IgM/IgG antibody reagents on the market by recruiting 111 participants suspected of having COVID-19 infection from March to May 2020 in a hospital. Taking RT-PCR as the diagnostic gold standard, the quantitative spectral LIFA platform could correctly detect all 12 COVID-19 patients. Concerning RT-PCR negative patients, all three antibody testing methods found positive cases. The optical-based platform exhibited the ability of early detection of immunoglobulins of RT-PCR negative patients. There was an apparent trend that elevation of IgM levels in the acute phase of infection; then IgG levels rose later. It exhibited the risk of a false-negative diagnosis of RT-PCR in COVID-19 testing. The significant detection ability of this new optical-based platform demonstrated clinical potential. The coronavirus disease COVID-19) is the global public health challenge currently persisting at a grand scale. It first presented a local outbreak of pneumonia of initially unknown pathogens in Wuhan  in December 2019 [9 is the Since the COVID-19 pandemic outbreak, the effectiveness of screening tests is one of the critical issues. To contain the pandemic, fast and reliable tests for COVID-19 are in urgent need worldwide. Reverse transcriptase-polymerase chain reaction (RT-PCR) and IgM/IgG enzyme-linked immunosorbent assay (ELISA) are the two major test types for diagnosing viral infections , 3. RT-PSerological tests were primarily employed to aid nucleic acid tests in the past, providing an extended window for detecting active and recent infections. Yet, with the advancement of vaccine developments, these serological tests possess another crucial function: determining changes in the recipient’s antibody level before and after vaccination. Meanwhile, the US Food and Drug Administration (FDA) has urgently approved an emergency use authorization (EUA) for plasma therapy . Plasma At present, further improvement of the accuracy of test reagents on the market can be achieved, with most reagents lacking the quantitative ability of COVID-19 antibodies. Quantitative knowledge is beneficial for determining the current phase of infection of a patient. It is necessary to have a method that meets the rapid quantitative detection of antibodies to assess the body’s immune response from natural COVID-19 illness or vaccines’ effects. Here, a simple platform that could be easily performed outside the laboratory would be beneficial. For this reason, a Lateral flow immunoassay (LFIA) coupled with a portable spectrometer  was developed.Further, an attempt was made to integrate a newly designed spectrometer to the COVID-19 test strip procedure; this augmentation provides the quantitative capacity to a LFIA. This detection method can quickly produce results in 5–10 minutes. The developed product was compared with several other serological IgM/IgG antibody reagents on the market, hoping to arrive at a relatively objective conclusion.The present study was approved by the Joint Institutional Review Board (JIRB) of the Taipei Hospital, Ministry of Health and Welfare (IRB numbers: TH-IRB-0020-0011). The present study was conducted following the relevant regulations and guidelines. 111 subjects aged 20–79 years old suspected of having COVID-19 infection  were recruited from a hospital in Taipei from March to May 2020. All study subjects have provided informed consent. Participants completed a survey questionnaire that includes demographic information: birth year, travel, contact, cluster, education levels, occupation, family income, alcohol, drug use, and lifestyle habits. Clinical symptoms such as cough, sore throat, general soreness, etc., were also collected. In addition, blood samples and swab specimens were collected at the hospital. Patients suspected of having COVID-19 disease premised on relevant medical history and symptoms were included in this study. The exclusion criteria included (1) Participants who had nasal or oral structure problems for sampling . (2) The participant was deemed unsuitable due to the present illness (such as undergoing Cardiopulmonary Resuscitation). Nasopharyngeal or oropharyngeal swab specimen was collected from each patient and sent for Reverse transcriptase-polymerase chain reaction (RT-PCR) testing. Blood samples of patients were collected on 0–30 days after symptom onset and examined by the LFIA.The RT- PCR was performed by LabTurboTM AIO SP-qPCR Dual-Panel Automation System. It uses one SARS-CoV-2 specific primer pair and probe that explicitly recognizes the SARS-CoV-2 RNA sequence (N1 gene) and is designed to achieve high sensitivity (LOD 20 copies/ml) the examination.Relying on immunochromatography principles, all COVID-19 rapid test strips in the present study used colloidal gold-conjugated antibodies to detect human IgG (Immunoglobulin G) and IgM (Immunoglobulin M) antibodies. A nitrocellulose strip was pre-coated with perpendicular recombinant viral COVID-19 protein lines that could capture reagents in this device. As the applied sample flowed down the strip, anti-COVID-19 IgG and IgM antibodies would form colloidal gold antibody-conjugated IgG and IgM complexes. The perpendicular IgG and IgM lines of the test strip captured these complexes, upon which colored bands would be presented. Moreover, although antibody levels vary between individuals, in most cases, the IgM is potentially helpful for detecting recent infection, which usually becomes undetectable in the weeks following infection. In contrast, the presence of IgG antibodies often indicates a past infection as it generally does not appear until 7 to 10 days and may last for months or years after infection.LFIA is a paper-based in situ detection platform that is characteristically inexpensive, easy to use, and operated by a non-healthcare provider. While LFIA is an ideal point-of-care test, the conventional colorimetric LFIA has an unsatisfactory limit of detection (LOD). Biochemical manufacturers provide us with different concentrations of standard samples. We obtain the value (α) by spectrometer system then establish a standard curve of IgG  and IgM Then we used the LOD formula to calculate the SARS-CoV-2 IgG and IgM detection limit, respectively, where reflectance value is converted from the raw spectrum. The content of our article will explain in detail how to learn positive and negative thresholds. The cutoff threshold (α = is at 1.05) is set with COVID-19 clinical positive and negative sera. LOD spiking test and clinical α cutoff test are two independent experiments. The subtle color variations are difficult to differentiate visually . TherefoThe chromophores on paper test strips emit complex spectra at various wavelengths that a spectrometer can readily detect to provide precise and quantifiable information. Hence, coupling the LFIA with the portable spectrometer improves sensitivity and enables quantitative analysis .Examiners could perform quantitative spectral analysis by using a spectrometer coupled with a test strip. An app of smartphone activated the automatic scanning of the rapid test strip. Full-spectrum antibody reflex optical signals were acquired from the spectral optical module to analyze COVID-19 IgG/IgM full-spectrum antibody distribution and concentration with standard quantification. The results can be used in conjunction with clinical timetables to analyze and track the spread of COVID-19. The quantitative spectral LFIA platform (SpectroChip Inc.) detects IgM/IgG antibodies from test kits .Designed for use with a LFIA COVID-19 IgG rapid test cassette, this spectra reader provides high resolution (3–5 nm) results across a vast spectral range (300 to 1000 nm). The quantitative spectral LFIA uses immunoassay technology and the principle of chromatography to fix specific antibodies or antigens on nano-grade colloidal gold particles as a reagent coloring agent to detect whether there is an immune response in the specimen. The platform detects the antibody test strip’s reflectance spectrum, where the primary reflectance wavelengths are minimum wavelength, and the central reference wavelength is 650 nm. The α value was calculated via ranges at minimum reflectance and reference wavelengths:p-value of <0.05 was considered statistically significant. Statistical analysis was executed using SAS .Data were expressed as mean ± standard deviation for normally distributed, continuous variables and proportions for categorical variables. Continuous variables were analyzed utilizing a two-tailed t-test, while discrete variables were compared using a Chi-square test. The difference in sensitivity or specificity of the antibody test was compared with RT-PCR through the McNemar test. A RT-PCR test confirmed 12 PCR-positive patients and 99 PCR-negative patients. There was no significant difference in demographic characteristics and symptoms between groups .The performance of the five types of LFIA was highlighted in The sensitivity of the other four assays increased after nine days of symptoms onset. . All assays achieved 100% sensitivity in samples collected after at least 14 days following initial symptoms. The ACE and TBG assay sensitivities were 91.67% (11/12) and 83.33% (10/12) for 12 positive samples collected 0–30 days post-symptom onset. However, the one false-negative blood sample in the ACE and TBG tests was caused by a hemolysis of specimen in this patient (the specimen was collected on the third day of symptoms). By taking RT-PCR as the gold standard of diagnosis, the overall sensitivity and specificity of LFIA could be determined via blood samples collected for one-month duration after symptoms onset.Among the 99 RT-PCR negative patients, the BioMedomics and ACE Biolabs anti-COVID-19 IgM and IgG assays were negative in 95.96% (95/99) and 97.98% (96/99) of samples, respectively .The quantitative spectral LFIA platform (Spectrometer +ACE Biolabs) employed the high sensitivity LFIA of ACE to provide precise assay results. The assay was quantitative in design, and the mean α value set for COVID-19 positive patients was over the LOD in any convalescent-phase sample with 100% sensitivity see . ConfideThe platform’s sensitivity was good in samples collected 0 to 4 days post-symptom onset . Further, 100% sensitivity was achieved in samples collected for a one-month duration following initial disease manifestation. The P-value showed a significant difference between COVID-19 negative and COVID-19 positive patients (p<0.001). The temporal kinetics of the anti-COVID-19 antibody response of COVID-19 positive patients for each of the five evaluated assays and spectra analyzer was exhibited , both inThe LFIA of ACE had the lowest COVID-19 screening false negative in the early stage of infection. In addition, the spectra analyzer of the spectrometer improved the lower limit of detecting immunoglobulins .As shown in The analytical platform of Spectrometer +ACE Biolabs can attain a detection limit of 0.362 ng/mL for the human anti-spike protein IgG/IgM, with only a drop of blood needed to produce a result in 5–10 minutes. It is beneficial in assessing the patient’s antibody levels after vaccination or infection and can play an essential role in clinical practice.In About RT-PCR negative patients, all three antibody testing methods found positive cases. One explanation is that there may have been patients who have been infected with COVID-19 that were missed by the RT-PCR test. In most patients, viral RNA often becomes detectable as early as day one and peaks within the first week of symptom onset. To be specific, the detection of viral nucleic acid by RT-PCR requires sufficient quantities of high-quality viral RNA, which is sometimes difficult to obtain due to variances in sampling technique, patient viral load, the timing of infection, and the onset of symptoms. Further, RNA samples are vulnerable to degradation. Well-trained scientists are required to perform PCR utilizing complex laboratory equipment . SimultaSimultaneously, there is a scarcity of information regarding the detection limit of serological assays than RT-PCR. Notably, serology testing may overcome some limitations of RT-PCR. In rare situations where RT-PCR is not possible, serological testing could be adopted. Phlebotomy is less technique-sensitive and produces minor sample variance. Blood antibodies are more stable than viral RNA collected by swabbing, and blood can be readily kept and tested in a device. A non-healthcare provider is often capable of operating a serology test , 13.The FDA implied that a negative RT-PCR test result does not wholly exclude COVID-19 infection. Thus, healthcare providers should not treat RT-PCR as a single element for diagnosis, and re-taking a test is reasonable in the presence of any doubt . An addiMost reports of COVID-19 of antibody reaction are skewed toward severe and hospitalized cases. In asymptomatic and presymptomatic patients, the detection effectiveness is not so clear. Notably, 10 out of 175 PCR-confirmed patients in one study had mild symptoms of COVID-19 and eventually did not develop antibodies, raising concerns regarding the use of serological antibody tests for screening . A likelThe platform adopted in the present research can produce results in just 5–10 minutes without a blood draw. It is much faster than RT-PCR, which takes about 1.5 hours to run an exam. Detection could be conducted via just a drop of blood, increasing people’s willingness to cooperate and reduce errors caused by the sampling of the throat or nasopharyngeal swabs. Besides, the detailed IgM and IgG values would reveal the disease’s progress. The serological antibody tests could fill up the missing gap of RT-PCR as an auxiliary role in the Covid-19 pandemic. Moreover, in remote areas where performing RT-PCR is inconvenient, rapid antibody screening is also advantageous.In the early stages of infection, the reagent bars representing positive results are not apparent because of insufficient antibodies. Therefore, the LOD would be inferior, with only the naked eye being used to judge the outcome . For this reason, machine detection of the reagent result by α value was developed. The machine adopted in the present article can be used for both antibody and antigen detection. However, the antibody exam function was chosen for comparing the ability with other rapid serology antibody screening reagents.The lack of positive cases is one of the limitations of our study. However, statistical significance was achieved, and the usefulness and accuracy of the test were demonstrated. The prevalence of Covid-19 positive cases was scarce in Taiwan when it was the initial outbreak. In terms of the newly developed test method, fewer patient numbers were inevitable since this method was not promoted. Meaningful improvement of sensitivity and specificity was observed after augmentation of the spectrometer to the LFIA.The is another limitation of spectrum bias. We assumed that sensitivity was highest for severe infections and considered a range of values of asymptomatic and mild infections . The subThis LFIA, coupled with a portable spectrometer, demonstrated improved sensitivity and enabled quantitative analysis. The present research is predicated on objective comparisons with similar serological immune assays on the market. Different periods after diagnosis were tracked to reveal the changes in results due to variation in antibody levels.This study’s platform showed high sensitivity and specificity in detecting trace amounts of COVID-19 antibody IgG/IgM. Optical interpretation  of results by quantitative α index improved the accuracy. Both Nucleocapsid (N) + Spike (S) solid-phase antigen detection also increased the sensitivity. This quick screening test could accurately measure antibody levels at the very beginning of the disease. It could be a useful evaluation tool for evaluating the natural course of the illness or antibody response from vaccines for COVID-19.S1 FileWe use this questionnaire to evaluate patients’ basic information, contact history, and symptoms associated with Covid-19.(PDF)Click here for additional data file.


IntroductionDifferent factors are critical when assessing COVID-19 mortality, and can explain why severity differs so widely among populations. However, there is little information regarding prognostic factors and mortality in COVID-19 from Latin American countries.ObjectivesTo determine prognostic factors in hospitalized COVID-19 patients and to evaluate the impact of tocilizumab use in patients with hyperinflammatory syndrome and severe disease defined by the National Early Warning Score 2 (NEWS2) with a value greater than or equal to seven points.Materials and methodsThis retrospective cohort study included hospitalized COVID-19 patients from May to July 2020. A multivariate logistic regression analysis was performed to determine independent factors associated with mortality.ResultsA total of 136 patients required hospital admission. In-hospital mortality was 39.7%. Mortality was observed to be potentiated by older age, LDH serum levels and the presence of type 2 diabetes mellitus. Lymphopenia and lower PaO2/FiO2 ratio were more common in these patients. Similarly, patients who died were classified more frequently with severe disease. The independent factors associated with in-hospital mortality were age greater than 65 years, type 2 diabetes mellitus, NEWS2 greater than or equal to seven points and LDH greater than 400U/L. The use of Tocilizumab alone was not related with decreased in-hospital mortality. Subgroup analysis performed in patients with hyperinflammation and severe disease showed similar results.ConclusionsCOVID-19 mortality in hospitalized patients was high and mainly related with older age, comorbidities, LDH and the severity of disease at hospital admission. The coronavirus disease 2019 (COVID-19) was classified as a pandemic in February 2020 . The risDexamethasone has demonstrated a clinical impact reducing mortality in hypoxemic patients that require hospital admission . MoreoveThere is little information regarding mortality and prognostic factors in COVID-19 from Latin América. Thus, this study aimed to determine prognostic factors in Colombian hospitalized COVID-19 patients and to evaluate the impact of tocilizumab use in patients with hyperinflammatory syndrome and severe disease defined by the National Early Warning Score 2 (NEWS2) greater than or equal to seven points.This is a retrospective cohort study of hospitalized COVID-19 patients admitted, from May 20th to July 31st in a convenience sample, to the Hospital Universidad del Norte, a 136-bed university hospital in Soledad, Colombia. Clinical-suspected and laboratory-confirmed COVID-19 patients were included. A clinical-suspected COVID-19 case was defined as a patient with upper or lower respiratory tract symptoms with compatible radiological computerized tomography (CT) lung scan  and no other etiologies documented. Laboratory-confirmed COVID-19 case was defined as a clinical-suspected COVID-19 case with positive results in real-time polymerase chain reaction (RT-PCR) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). RT-PCR for SARS-CoV-2 was performed from respiratory samples in all patients. The study was approved by the Institutional Ethics Committee (ID213-2020).Patients were divided into two groups: (i) patients who survived; and (ii) patients who died. The independent variables were clinical characteristics and laboratory values. The treatment of COVID-19 pneumonia was according to institutional protocol and included antibiotics, oxygen, steroids and tocilizumab. Regarding tocilizumab, it was used in patients with hypoxemia and elevated markers of inflammation. Tocilizumab was not administered to patients with suspicion of concomitant bacterial infection. The administered intravenous dose was 400 mg for individuals less than 75 kg and 600 mg for those greater than or equal to 75 kg. All tocilizumab doses were given within the first 24 hours of hospital admission.To address illness severity we used the NEWS2 . Severe Statistical analysisThe categorical variables were reported as frequencies and percentages and the continuous variables as median and interquartile range (IQR). Normal distribution was tested. The Mann-Whitney U-test was used for comparing continuous variables and chi-square or Fisher's test for qualitative variables. To determine independent factors associated with in-hospital mortality, a multivariate analysis was performed. The model included important clinical variables, including tocilizumab, and those with significant difference between groups. Results of multivariate analyses were reported as odds ratio (OR) and 95% confidence intervals (CI). The goodness of fit of the multivariate model was assessed by the Hosmer-Lemeshow test and the accuracy by the area under receiver operating characteristic (AUROC) curve. We performed subgroup analyses according to the presence of hyperinflammation and severe disease using NEWS2 ≥7. The threshold for statistical significance was defined as a two-tailed p < 0.05. All analyses were performed using the SPSS software, version 25 . During the study period, 136 patients with COVID-19 were admitted to the hospital. Regarding RT-PCR results, 113 (83.1%) were classified as laboratory-confirmed COVID-19 and 21 (16.9%) as clinical-suspected COVID-19.Characteristics of the cohortClinical features and laboratory findings of all patients are shown in Table Factors associated with mortalityPatients who survived were compared with patients who died Table . PatientA logistic regression analysis was conducted to determine independent factors associated with in-hospital mortality Table . The indSubgroup analysesSubgroup analyses were performed in patients with hyperinflammatory syndrome and with severe disease. In the multivariate regression analyses, the factors associated with mortality were similar to those in the general population. Tocilizumab was not a protective factor related with in-hospital mortality neither in hyperinflammatory syndrome  nor in NEWS≥7 subgroups .In this retrospective cohort study in hospitalized patients with COVID-19, mortality was high and mainly related with older age, type 2 diabetes mellitus, high levels of LDH and the severity of disease at hospital admission. The use of tocilizumab was not related with better prognosis, even in patients with severe disease and patients with hyperinflammatory syndrome.The present results are consistent with data from studies that evaluated prognostic factors in hospitalized patients with COVID-19. In the univariate analysis of this study, age, type 2 diabetes mellitus, dyspnea, PaO2/FiO2 ratio, serum levels of d-dimer, LDH and creatinine, and lymphocyte count were factors associated with a higher risk of in-hospital mortality. In this regard, there is evidence that advanced age, comorbidities, abnormal inflammatory and organ injury circulating biomarkers were more frequently found in patients with an adverse clinical outcome . Thus, cWe documented a higher mortality among hospitalized COVID-19 patients (39.7%) compared with that found in other studies (9%-25%) . In addiRegarding treatment, studies evaluating interleukin 6 (IL-6) receptor blockade in COVID-19 patients have documented controversial results. Multiple observational studies have shown a discrete mortality reduction in patients with moderate to severe pneumonia due to COVID-19 -20. SimiThe strengths of this study are the comprehensive clinical data gathered. This information allowed us to identify specific subgroups of patients. In addition, we performed a multivariate analysis to control confounding factors. The accuracy of the multivariate model was good, as evaluated by the area under the ROC curve. However, our study has some limitations that should be acknowledged. The selection bias restricts the results analysis and the limited sample of patients undermines the power of the study. Moreover, there is no standardized criteria for hyperinflammatory syndrome in COVID-19 and almost every study has a different definition. However, we used the most recent validated COVID-19 hyperinflammatory syndrome criteria in our analysis . AnotherIn conclusion, COVID-19 mortality was high during the first pandemic wave. The main factors related with mortality were older age, comorbidities as diabetes mellitus, high levels of LDH and the severity of disease at hospital admission according to NEWS2. Tocilizumab had no relation with mortality in global population and in subgroups. Studies should assess if, among COVID-19 patients, there are different inflammatory patterns, such as hyperinflammation, and if different treatment approaches may improve outcomes in specific subgroups of patients.


Statins are widely used to treat people with metabolic and cardiovascular disorders. The effect of statins on coronavirus disease 2019 (COVID-19) is unclear. To investigate the association between statins and COVID-19 outcomes and, if possible, identify the subgroup population that benefits most from statin use.A systematic review and meta-analysis of published studies that included statin users and described COVID-19 outcomes through 10 November 2020. This study used the generic inverse variance method to perform meta-analyses with random-effects modelling. The main outcomes were evaluation of the need for invasive mechanical ventilator (IMV) support, the need for intensive care unit (ICU) care and death. All outcomes were measured as dichotomous variables.I2=72%) and the need for IMV  but was not linked to the need for ICU care . Subgroup analysis further identified five types of studies in which statin users had even lower odds of death.A total of 28 observational studies, covering data from 63,537 individuals with COVID-19, were included. The use of statins was significantly associated with decreased mortality  to validate the benefits of statins on COVID-19 outcomes.The use of statins was significantly associated with a reduced need for IMV and decreased mortality among individuals with COVID-19. Statins may not need to be discontinued because of concern for COVID-19 on admission. Further randomized controlled trial (RCTs) are needed to clarify the causal effect between statin use and severe COVID-19 outcomes. Coronavirus disease 2019 (COVID-19) has been a tremendous threat to human life since the pandemic began. People at high risk of unfavourable outcomes caused by COVID-19 include elderly persons and individuals who have chronic metabolic and cardiovascular comorbidities . WhetherStatins are primarily used to lower serum lipids by inhibiting HMG-CoA reductase. In addition, statins are also known to have pleiotropic effects, including (1) anti-inflammation, (2) immunomodulation, (3) upregulation of angiotensin-converting enzyme 2 (ACE2) receptor expression, 4) antithrombosis and (5) antioxidation  antithro. Some ofAn increasing number of researchers are focussing on the relationship between statins and COVID-19 . CurrentThis study aims to explore the relationship between statins and COVID-19. Through a comprehensive systematic review and meta-analysis, we expect to answer two clinical questions: (1) will the use of statins lead to a favourable/poor outcome of COVID-19, and (2) can statins decrease the mortality of COVID-19 in a subgroup of statin users? The study results will provide valuable information to health care workers and guide the choice of potential participants in future RCTs.Supplementary Table S3). We searched the PubMed, Cochrane Library, Embase and MEDLINE Online databases for relevant articles published through 10 November 2020, using the keywords and related MeSH terms as follows: “COVID-19”, “statin’ and “HMG-CoA reductase”. There was no restriction on language or publication status. The reference lists of eligible studies were also manually searched to identify any additional relevant studies. Detailed search strategies are described in Supplementary Table S1.This study was a systematic review and meta-analysis conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations   formulation. The population were individuals with positive/confirmed COVID-19; the intervention was the use of statin before or during the hospital course of COVID-19; the comparators were those who did not take statin before or during COVID-19. This study set up three outcomes to evaluate the relationship between statin use and COVID-19, including intensive care unit (ICU) care, use of an invasive mechanical ventilator (IMV) and death. Any study that described at least one of the outcomes was included for assessment and analysis. Eligible study designs included RCT, cohort, clinical trial, case-cohort and cross-over design. The following types of articles were excluded, including review articles, hypothesis, case reports, articles in a non-English language, articles focussing on paediatric populations (17 years of age or younger), articles with a Newcastle–Ottawa scale (NOS) score of 6 or below, articles providing inadequate information and articles not relevant to the study goal. Two investigators (TCP and PLT) independently performed a systematic review using the same criteria and included studies on the basis of agreement. Upon disagreement, a third investigator (PCL) joined and helped make the final decision.Eligible studies were first deduplicated by EndNote X9. Then, two investigators (KSW and PLT) independently extracted data from the included studies using an established data collection form. Collected variables included the first author’s name, publication year, study country, study design, sample size, study period, demographics of participants, follow-up duration, diagnostic method of COVID-19, study quality and outcomes. We also contacted corresponding authors to gather missing data when needed. Both raw data and results presented as relative risk/odds ratio (OR)/hazard ratio were included. If a study provided unadjusted and adjusted results, we extracted the adjusted results. The case fatality rate (CFR) of each study was calculated using the number of deceased individuals divided by the number of all individuals . Any disThis study used the Cochrane risk of bias tool  and NoS I2 analysis. An I2> 50% indicated the existence of substantial heterogeneity, and subgroup analysis was further performed. All meta-analyses were conducted by Review Manager version 5.3 software  provided by Cochrane. In addition, a funnel plot was generated with Stata Statistical Software version 15.0  to evaluate the possibility of publication bias.This study used the generic inverse variance method to perform meta-analysis since some of the enrolled studies reported results with an inverse design. We conservatively chose random effect modelling for analysis since differences in the study population and study design between studies were expected. If an article presented both unadjusted and adjusted results, we retrieved the adjusted results for meta-analysis. This study used the odds ratio to present the overall effect estimates in forest plots and subgroup analysis. The results of the meta-analysis are shown as forest plots with heterogeneity tested by Supplementary Figure S1). Out of the 28 studies, six studies reported irrelevant outcomes to our study aims [A total of 502 articles were identified by searching the database and related websites. After deduplication, record screening and eligibility assessment, 28 studies remained for qualitative synthesis, as shown in udy aims ,31,35. Tudy aims ,36–44.n = 26) [n= 2) [Supplementary Table S2). The funnel plot showed an asymmetrical distribution of the points, suggesting a lack of publication bias  .All 28 articles included for qualitative synthesis were observational studies. The participants were individuals who were confirmed to have contracted COVID-19, were hospitalized for management and underwent a follow-up period of at least 28 d after study entry. Among these selected studies, 11 were conducted in the United States (USA) ,38,39,41n = 26) ,38–44 or) [n= 2) ,37 by thp=.73, I2= 66%)   . Four st, I2=0%) .p= .01, I2=72%)  .I2=72%), we performed subgroup analysis to explore the source of heterogeneity. We identified five subgroups of studies in which statin users had even lower odds of death, including studies that (1) enrolled participants not limited to those diabetes mellitus (DM) only , (2) had a CFR of lower than 20% , (3) presented adjusted outcomes , (4) were conducted in Asia  and (5) emphasized that the timing of statin use was during hospitalization . The heterogeneity in these five subgroups of studies was lower than that before the subgroup analyses. On the other hand, one single study that included only individuals with DM revealed that statin use was associated with increased COVID-19 mortality . Since substantial heterogeneity existed in the meta-analysis of statins and COVID-19 mortality  production.According to the study results, statins were associated with a reduced need for mechanical ventilators but did not influence the possibility of ICU admission. Both of the above results had low heterogeneity. Two possible reasons may explain that. First, the criteria for the use of mechanical ventilators or ICU care might vary among different areas, hospitals and even physicians. Insufficient medical resources could also play a role in some cohorts. Second, statins provide lung protection and thus decrease the need for IMV. However, statins could not reduce other nonpulmonary events, such as cardiovascular complications or sepsis. The mechanism of lung protection is most likely Although one of our subgroup analyses showed that in-hospital use of statins had a greater benefit, we cannot interpret that to mean that starting statins on admission to treat COVID-19 was helpful. According to clinical experiences, most in-hospital statin users were already taking statins before contracting COVID-19. Besides, the majority of articles included in our study emphasized the use of statin was prior to COVID-19 infection, and the results showed statin had a trend of protective effect from getting COVID-19. Retrospective observational studies had an intrinsic limitation in recording clear-cut timing of statin use among a large group of participants. However, our results at least confirmed that statin users can continue taking the medication during COVID-19 infection.vs. nonusers exhibited substantial differences. Furthermore, some studies reported only crude odds ratios, which might lead to an even more significant bias. Second, the type, dose and duration of statins were by no means identical across studies. The majority of the studies did not detail the prescriptions of statins. Thus, even though the current evidence showed that statins might be beneficial for patients with COVID-19, it was almost impossible to provide a clear suggestion about the drug name, dose and duration. Third, a substantial portion of participants in this study had comorbidities of diabetes or cardiovascular diseases, and might have taken other concomitant medication, such as metformin, ACE inhibitors and angiotensin II receptor blockers (ARB). Metformin has shown to be associated with reduced COVID-19 mortality [There are several limitations to the study. First, all articles included in our meta-analyses were observational study designs. The baseline demographics of statin users ortality , and ACEortality . The infOur results demonstrated that the use of statins was associated with a reduced need for IMV and mortality rate among individuals with COVID-19. Subgroup analyses further identified individuals enrolled in studies with a lower mortality rate who benefitted more from statins. This study also confirmed that statins could be safely used during COVID-19 hospitalization.Click here for additional data file.