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Introduction

Since December 2019, there has been an outbreak of pneumonia caused by a new type of coronavirus in Wuhan, China, and named coronavirus disease (COVID-19) by the World Health Organization.15 Since 1 January 2020, more than 90 million confirmed cases and more than 2 million deaths have been reported worldwide. With regard to ICU admission in COVID-19, ARDS, respiratory failure, Sepsis, heart failure, and septic shock, multi-organ failure were the frequently observed complications. However, the reported incidence of ARDS is approximately 15%30%, higher than that of other organ injuries, such as acute myocardial injury and acute renal injury.6,7 Acute respiratory distress syndrome (ARDS) is a clinical and pathophysiological syndrome caused by various intrapulmonary and extrapulmonary factors, and its most important feature is refractory hypoxaemia.8 To date, the mortality rate of ARDS in hospitalized patients remains as high as 50%.9,10 For some unknown reasons, some patients with mild COVID-19 will rapidly progress to ARDS within a week. Although they can be admitted to the intensive care unit (ICU) for treatment, the mortality rate remains high.11 ARDS caused by COVID-19 seems to have a worse prognosis than ARDS caused by other causes. The mortality rates in the ICU and hospital for typical ARDS were 35.3% and 40.0%, respectively.10 For coronavirus-associated acute respiratory distress syndrome (CARDS), the mortality rate was between 26% and 61.5%.

The early detection of the likelihood of CARDS will help to appropriately identify and classify those who need to accept ICU treatment earlier. However, there are few studies on prediction models for the occurrence of CARDS. Therefore, a predictive model is needed to evaluate the probability of CARDS according to the easily obtainable and quantifiable clinical indicators of the patient. Therefore, we developed a nomogram for predicting the risk of CARDS that can help screen patients who are likely to develop severe respiratory distress in clinical settings.

We selected patients with COVID-19 admitted to Tongji Hospital at Huazhong University of Science and Technology (Wuhan, China) between 11 February and 31 March 2020. The inclusion criteria were as follows: (1) confirmed diagnosis of COVID-19 by detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) via reverse transcriptionpolymerase chain reaction and (2) age >18 years. All eligible patients were randomly assigned to one of two groups in a 3:1 ratio (training cohort and validation cohort, respectively). The study complied with the edicts of the 1975 Declaration of Helsinki and was approved by the principal investigator center, Institutional Review Board of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (No.:(2020) Linlun-34th). Written informed consent was obtained from patients or their immediate relatives.

We retrospectively reviewed the first-hand clinical database collected within 48 hours of hospitalization. Baseline characteristics, routine laboratory tests and outcomes were recorded. Clinical laboratory tests included fasting plasma glucose (FPG), C-reactive protein (CRP), white blood cell (WBC) count, absolute lymphocyte count, platelet (PLT) count, alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin, serum creatinine (Scr), BUN, D-dimer, cTnI, IL-6, LDH, and neutrophil-to-lymphocyte ratio (NLR). All laboratory assessments were conducted at each visit in Tongji Hospital by the same trained technicians, strictly following the clinical guidelines.

The endpoint event was whether patients with COVID-19 had progressed to ARDS. All patients with ARDS were diagnosed according to the Berlin definition.12 The diagnosis of COVID-19 was based on the Guidelines for Diagnosis and Treatment of Novel Coronavirus Pneumonia (7th version) released by the Health Commission of China. For the diagnosis of severe COVID-19, at least one of the following criteria should have been met: (1) respiratory distress, shortness of breath with respiratory rate 30 times/min; (2) arterial oxygen saturation (resting status) 93%; (3) partial pressure of oxygen/fraction of inspiration O2 (PaO2/FiO2) 300 mm Hg; and 4) have respiratory failure and need for mechanical assistance; shock; extra pulmonary organ failure, intensive care unit is needed. Otherwise, the patients were defined to have non-severe COVID-19.

According to the results of univariate and multivariate logistic regression analysis, the risk factors of CARDS have been found. Then, the nomogram was developed using the rms and foreign R packages. The rms R package, including nomogram function, was used to calculate equation and draw nomogram. The foreign R package was used to read and write data. Firstly, the regression equation was derived from the multivariate logistic regression. Nomogram function transformed the coefficients of logistic regression results in the multivariate logistic regression analysis, and eventually transformed it to nomogram formula. Secondly, in this study, as the most significant risk factor of CARDS, cTnI would be given to the maximum score of 100 points. Based on the cTnI, other risk factors were assigned corresponding score according to the ratio in the nomogram formula. The data was then displayed in the form of nomogram. The corresponding calibration plot, ROC analysis and decision curve analysis (DCA) were performed in the training and validation cohorts to assess the discrimination, the degree of consistency, and the quality of clinical applicability of the nomogram model. The calibration plots were drawn using the rms and foreign R packages. The rms R package, including calibrate function, was used to calculate equation and draw calibration plots. The function of the foreign R package was same as the aforementioned. The calibration evaluation of the predictive model was an important indicator for evaluating a certain individual ending event probability. It reflected the degree of consistency of model predicted probability and actual probability. The curve Ideal meant standard curve that represents the predicted probability and the actual probability completely matched. And the curve Logistic calibration represented a calibration curve of logistic regression predictive model. The consistency was judged by the degree of coincidence of two curves. The DCA curves were drawn using the rms, foreign, and nricens R packages. Many functions were included in the rms R package, participated in regression modeling, validation, and graphics. The function of the foreign R package was same as the aforementioned. The function of the nricens R package was that calculating the net reclassification improvement (NRI) for risk prediction models with binary data. A significant concept in DCA was probability threshold, namely, a level of diagnostic certainty above which the patient would choose to be treated. The net benefit was determined by calculating the difference between the expected benefit and the expected harm associated with each proposed testing and treatment strategy. The curve None meant that all people are not treated, and the net benefits are 0. The curve All was drawn as if all patients receive treatment irrespective of laboratory test results. For any given probability threshold, the curve with the highest net benefit at that threshold was the best choice. Multiple cross-validation results are shown in Table S3.

Schematic figure of the data processing workflow is shown in Figure S1. Because missing values will lead to deviations in the results to a certain extent, before data analysis, multiple imputations were performed on missing data to obtain appropriate values. Discrete variables are reported as frequency and proportions, and continuous variables are reported as the meanstandard deviation or median with interquartile range as appropriate. Differences between the two groups were tested using the independent-sample t-test, MannWhitney U-test, or chi-square test as appropriate, and all analyses were considered significant at P values of <0.05 (two-tailed). Clinical data were processed using SPSS version 23 (IBM Corp., Armonk, NY, USA) and R version 3.4.4.

Between 11 February and 31 March 2020, 261 patients were hospitalized for COVID-19 diagnosis. Demographic and clinical characteristics of the patients are described in Table 1. A total of 261 patients were randomly divided into the training and validation cohorts in a 3:1 ratio (197 from the training cohort and 64 from the validation cohort). In the training cohort, the most common clinical symptoms of patients with COVID-19 at the time of onset were sore throat (84.26%), expectoration (48.22%), cough (19.8%) and fever (20.81%). Among the patients in the validation cohort, the proportions of the aforementioned symptoms were similar to those in the training cohort. In the training and validation cohorts, the proportion of patients with severe COVID-19 was similar to that of patients with non-severe COVID-19. The same was true for patients with and without ARDS. Within 48h of admission, the median levels of FPG, ALT, AST, Scr, BUN, IL-6, and LDH and WBC, absolute lymphocyte, and PLT counts were within the normal range. However, the median levels of CRP, D-dimer, and cTnI and NLR were elevated.

Table 1 Baseline Characteristics of Patients in Training Cohort and Validation Cohort

Based on the basic clinical information of the patients, we explored which factors are risk factors that cause the patient to develop ARDS. Therefore, univariate and multivariate logistic regression analyses of the aforementioned clinical indicators were tested in all 261 patients to identify the risk factors for ARDS. Before the regression analysis, to simplify the scoring system, we converted continuous variables into classified variables. Variable conversion was calculated from the cut-off value of the ROC curve of the variable. The continuous variable is converted into a classified variable with a cut-off value of zero, as shown in Table S1. In univariate regression analysis, factors such as age, FPG, CRP, WBC count, PLT count, D-dimer, and cTnI were closely related to the occurrence of ARDS (Table 2). However, in multivariate logistic regression analysis, only FPG, PLT count, D-dimer, and cTnI were directly and independently linked to the occurrence of ARDS. The regression equation established by multivariate logistic regression analysis is 2.54*FPG-1.13*Platelet+1.63*D-dimer+3.76*cTnI-12.47.

Table 2 Univariate and Multivariable Logistic Regression Analysis of the Training Cohort

In the aforementioned results, the risk factors for ARDS were identified. To formulate an optimal nomogram model, the individual performance of these factors was comprehensively evaluated using ROC analysis. As shown in Figure 1, the AUCs of FPG, PLT, D-dimer, and cTnI were 0.63, 0.72, 0.77, and 0.86 in the training cohort, respectively. In the validation cohort, the AUCs of FPG, PLT, D-dimer, and cTnI were 0.62, 0.54, 0.75, and 0.92, respectively. Therefore, a nomogram for predicting the probability of CARDS was preliminarily constructed using four factors: FPG, PLT, D-dimer, and cTnI (Figure 2). The associated concordance index was 0.93 (95% CI, 0.860.99), which indicated that approximately 93% of the probability of the diagnosis of CARDS would be correctly predicted by the nomogram model. These risk factors corresponded to the different scores of the nomogram in accordance with different weights in the aforementioned equation. After calculating the total score, we used it to draw a vertical line to obtain the probability of CARDS. The associated scores for the independent risk factors calculated by the nomogram in the corresponding situation are presented in Table S2.

Figure 1 ROC curves of the nomogram, FPG, PLT, D-dimer, and cTnI in the training and validation cohorts. (A) ROC curve in training cohort. (B) ROC curve in validation cohort.

Figure 2 The nomogram predicts the probability of hospitalized COVID-19 patients progressing to ARDS. The score for each value is assigned by drawing a line upward to the points line, and the sum of the four scores is plotted on the Total points line.

To evaluate the diagnostic performance of the model, we drew the ROC curve, calibration plot, and DCA curve of the training cohort. The ROC curve was mainly used to assess the discrimination of the model; that is, a larger AUC value represented a better diagnostic ability. We determined the AUC of the nomogram of the training cohort to be 0.93 (95% CI, 0.860.99) (Figure 1A). The calibration evaluation of the predictive model is an important indicator for evaluating a certain individual ending event probability. It reflects the degree of consistency of model predicted probability and actual probability, so it is generally called consistency. With poor calibration, the model is likely to be overestimated or underestimate the risk of disease. When the curve Logistic calibration is above the curve Ideal, the risk of the predictive model underestimated the risk of disease; when the curve Logistic calibration is below the curve Ideal, the risk of the predictive model overestimated the risk of disease. The calibration plot demonstrated an almost perfect agreement between the predicted probability and the observed outcome fitted to the ideal line (Brier score 0.058) (Figure 3A). Figure 4A illustrates the decision curves analysis for FPG, PLT, D-dimer, cTnI, and the nomogram model to predict the correct diagnosis of CARDS. The net benefit of the nomogram was better than any other factor between threshold probabilities of 590%, which ensured maximum clinical benefit. In addition, we evaluated the prediction model by further observing the statistical results of the validation cohort. The AUC of the nomogram was 0.92 (95% CI, 0.850.98) in the validation cohort. The calibration plot also displayed high consistency in the prediction of CARDS (Brier score 0.087) (Figure 3B). Due to the limitations of the number of people in the validation cohort, the nomogram in this group was worse than the training cohort in the DCA curve; however, the nomogram did not perform worse in any other single factor (Figure 4B).

Figure 3 Calibration plots for predicting the rate of ARDS in the training and validation cohort. (A) Calibration plot in training cohort. (B) Calibration plot in validation cohort.

Figure 4 The decision curves analysis curves for nomogram in the training and validation cohort. (A) DCA curve in training cohort. (B) DCA curve in validation cohort.

In this study, we developed and validated a nomogram for the early prediction of CARDS in patients with COVID-19. The nomogram based on FPG, PLT count, D-dimer, and cTnI had a discriminatory ability (C-index) of 0.93 (95% CI, 0.860.99) in predicting CARDS. CARDS itself has the characteristics of high mortality and difficult detection early.1315 Therefore, the early detection of CARDS and early intervention are particularly important in clinical practice.

Several studies have developed models that predict the diagnosis and prognosis of COVID-19.1619 It was reported that a serum fibrinogen level of 617 mg/dL in patients with COVID-19 may help to identify early those with ARDS.20 Zhang et al reported that older age was associated with poor condition and outcome in patients with COVID-19.17 However, there are few studies on prediction models for the occurrence of ARDS. Moreover, the existing research has not established a comprehensive predictive model. It has been reported that older age, initial pulmonary infiltration on a chest radiograph, and CRP are independent predictors of ARDS occurrence in patients with COVID-19 pneumonia.21 However, the previous study did not perform a comprehensive assessment of the scoring system of its predictive model; therefore, it is difficult to determine the clinical benefit of the model. Another study only clarified the risk factor analysis of patients with COVID-19 with ARDS.22 Our study analysed the risk factors for ARDS and established a prediction model. More importantly, we conducted a series of assessments for the discrimination, calibration, and clinical benefits of the model to determine its stability and clinical utility.

According to the aforementioned findings, there are four independent risk factors for CARDS, including FPG, PLT, D-dimer, and cTnI. In this study, higher FPG levels were associated with a greater probability of developing ARDS. Previous studies have shown that the FPG level in the death group was significantly higher than the survival group among patients with COVID-19; that is, an increase in plasma glucose level indicated a worse prognosis for patients.23 Even in patients without diabetes, the FPG level in the death group was significantly higher than the survival group. This shows that FPG is an independent risk factor for COVID-19 and has limited association with the patients diabetes history.23,24 Infection may trigger an inflammatory storm, which leads to insulin resistance and ultimately increases the FPG levels. SARS-CoV-2 virus may also directly attack the pancreas and increase the FPG levels.25,26 In the analysis of risk factors, the D-dimer levels were positively correlated with the occurrence of ARDS, while PLT levels were negatively correlated with it. The results of this study are consistent with those of other studies in terms of the characteristics of patients with COVID-19.27,28 Coagulation dysfunction seems to be common in COVID-19 and can be detected by increased D-dimer levels and decreased PLT levels.13,29 Our findings showed that the cTnI level was positively correlated with the probability of CARDS. In previous studies on the basic clinical information of patients with COVID-19, the cTnI level was reported to be significantly related to the severity of the disease; that is, high cTnI levels are positively related to the occurrence of ARDS and poor prognosis in patients30,31 In clinical practice, cTnI is usually used as a sign of heart damage. In patients with COVID-19 complicated by heart damage, the cTnI level was also significantly increased, indicating that the role of cTnI in predicting heart damage is also applicable to patients with COVID-19.31 The systemic inflammatory storm triggered by SARS-CoV-2 infection may be one of the causes of heart damage.32,33 Our study has several strengths. Firstly, the ARDS predictive model established by analysing our existing data has high sensitivity and clinical utility. Secondly, there are fewer evaluation results on the calibration and clinical utility of the predictive model.21,22 In this study, the randomly assigned and independent validation cohort combined with calibration plots and DCA curves can comprehensively and completely evaluate the accuracy, reliability, and clinical utility of this model. Moreover, the clinical indicators involved in this study can be obtained within 24 hours after admission and can be quickly scored and classified according to the patients laboratory results, which provides great help for the early recognition of CARDS.

However, this was a single-centre, retrospective study with a small sample size, and our study also has some limitations. Firstly, the individual characteristics of the studied patients affecting the results cannot be excluded. Secondly, due to the limitations of the sample, the number of patients in the validation cohort was relatively small, which makes the consistency poorer than the training cohort in the calibration plot. Thirdly, the range of threshold probability that has a net benefit is too small in the validation cohort.

Our research results showed that the predictive model of CARDS constructed through several simple, quantitative, and easily available laboratory indicators has good clinical significance. This may be helpful in promptly identifying whether patients with COVID-19 can progress to ARDS and adopting precise prevention and targeted treatment measures.

The study was approved by Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (No.:(2020) Linlun-34th). Written informed consent was obtained from patients or their immediate relatives. A few patients had serious clinical symptoms and could not sign their informed consent. Under emergency, attending doctor contacted the immediate relatives to finish informed consent through the phone and recorded the process. Then, the immediate relatives entered the hospital through the epidemic prevention procedure, and signed an informed consent.

This study was supported by Medical-engineering Cross Foundation of Shanghai Jiao Tong University grant 2019-nCoV research project (YG2020YQ30) awarded to JL-L, the National Natural Science Foundation of China (81770005) and (81970005) awarded to JL-L, Three-year plan for developing a public health system of Shanghai (GWV-10.2-XD03) awarded to JL-L.

The authors report no conflicts of interest in this work.

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13. Gibson PG, Qin L, Puah SH. COVID-19 acute respiratory distress syndrome (ARDS): clinical features and differences from typical pre- COVID-19 ARDS. Med J Aust. 2020;213(2):54. doi:10.5694/mja2.50674

14. Ferrando C, Suarez-Sipmann F, Mellado-Artigas R, et al. Clinical features, ventilatory management, and outcome of ARDS caused by COVID-19 are similar to other causes of ARDS. Intensive Care Med. 2020;46(12):22002211. doi:10.1007/s00134-020-06192-2

15. Gattinoni L, Coppola S, Cressoni M, Busana M, Rossi S, Chiumello D. COVID-19 does not lead to a typical acute respiratory distress syndrome. Am J Respir Crit Care Med. 2020;201(10):12991300. doi:10.1164/rccm.202003-0817LE

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19. Zheng Z, Peng F, Xu B, et al. Risk factors of critical & mortal COVID-19 cases: a systematic literature review and meta-analysis. J Infect. 2020;81(2):e16e25. doi:10.1016/j.jinf.2020.04.021

20. Di Micco P, Russo V, Carannante N, Imparato M, Cardillo G, Lodigiani C. Prognostic value of fibrinogen among COVID-19 patients admitted to an emergency department: an Italian Cohort Study. J Clin Med. 2020;9(12):4134. doi:10.3390/jcm9124134

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24. Huang Y, Guo H, Zhou Y, et al. The associations between fasting plasma glucose levels and mortality of COVID-19 in patients without diabetes. Diabetes Res Clin Pract. 2020;169:108448. doi:10.1016/j.diabres.2020.108448

25. Marik PE, Raghavan M. Stress-hyperglycemia, insulin and immunomodulation in sepsis. Intensive Care Med. 2004;30(5):748756. doi:10.1007/s00134-004-2167-y

26. estan M, Marinovi S, Kavazovi I, et al. Virus-induced interferon- causes insulin resistance in skeletal muscle and derails glycemic control in obesity. Immunity. 2018;49(1):164177.e6. doi:10.1016/j.immuni.2018.05.005

27. Sun Y, Koh V, Marimuthu K, et al. Epidemiological and clinical predictors of COVID-19. Clin Infect Dis. 2020;71(15):786792. doi:10.1093/cid/ciaa322

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nomogram for predicting the risk of coronavirus | IDR - Dove Medical Press

A tutorial to higher rankings for WordPress sites

This is the original WordPress SEO article since 2008, fully updated for 2021!

WordPress is one of the best content management systems when it comes to SEO. But even though it gets a lot right out of the box, theres much more that you can do to improve your performance. Its time to focus on WordPress SEO!

Optimizing your site using the tactics and best practices outlined in this article will help you improve your rankings, gain more subscribers or sales, and have a better website in general.

Because you should ingrain proper SEO in all aspects of your online marketing and PR, this guide covers quite a lot of ground! Its a long read, so feel free to use the table of contents below to jump around.

This article assumes that youre using our Yoast SEO plugin, which adds significantly more features and SEO tools to WordPress. If youre not already using it, you can set it up right away with our beginners guide to Yoast SEO. If you are on Shopify, please check out the Yoast SEO for Shopify app.

Read more: How much does Yoast SEO cost?

If youre using another SEO plugin, most of the principles will still apply. Of course, wed prefer you to switch over and make use of our potent WordPress SEO plugin, which is why weve written a migration guide for you. Its a straightforward process!

We have an epic article on that including hours of video. Find out how to make a WordPress website.

Out of the box, WordPress is a pretty well-optimized content management system. A basic setup can provide a strong foundation without extensive customization, theme optimization, and plugins. That said, there are a few things you can do with WordPress SEO to increase your chances of ranking, refine your workflow, and make sure your website is perfectly optimized.

By putting the right basic settings in place and applying a few simple techniques, you can ensure that you have a strong foundation to build upon!

Before you make any changes to your site, it is good to see where you are now. Theres a lot to gain from getting it right: running your website on a server with updated software at a web host that offers excellent performance. So ask yourself: on what hardware and software are your sites running? What is your hosting plan? Are you using a budget shared hosting provider, or have you invested in a dedicated hosting plan at a well-known web host that fine-tuned its servers for use with WordPress?

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According to WordPresss technical requirements page, the recommended hosting plan to run WordPress should include a modern version of PHP, MySQL or MariaDB, and HTTPS support. It is possible to work with older server software, but that is not recommended. If you check your Site Health, you can see the technical details of your installation. In addition, if you open the dashboard of your hosting provider, you should be able to see what type of plan you are on.

Remember, paying for good WordPress hosting pays dividends. Upgrading your hosting plan is one of the easiest improvements you can make.

While the numbers are going down quickly, many WordPress sites still run on outdated versions of PHP. One look at the WordPress stats reveals that some sites still run on a PHP version in the five series, while PHP 8.0 and up have been available for years.

Backward compatibility is cool and all, but its holding back WordPress as a technology and site owners from getting the most out of their sites. These old PHP versions dont receive any more security fixes and are thus increasingly vulnerable to attacks.

Luckily, the WordPress team has dropped support for anything older than PHP 5.6.20. Today, the project recommends running WordPress on at least PHP 7.4.

So, one of the most important things you can do to improve the performance and security of your site and thus, your WordPress SEO , is to upgrade your hosting environment to a modern version of PHP. There are a lot of benefits to this:

Now, this is something we all want. If youve checked your current hosting set-up in the previous section, you have an idea of what your site runs on now. If this shows outdated server software like PHP 5.5, it is good to update this, if possible.

However, take special care before doing so. Ask for help if youre not sure what you are doing.

Here are some steps to take:

We have a post that shows you how to set up a test environment for your WordPress site. WordPress.org has a post on the advantages of updating your PHP version and what to take into consideration when doing that.

Historically, adopting SSL (getting an HTTPS URL and a green padlock icon in the browser URL bar) was an optional tactic. Many sites, arguably, didnt need the extra level of security that SSL provides.

Today, having a valid SSL certificate installed is mandatory search engines may penalize sites without valid SSL certificates and setups (and/or show warnings next to their search results). Google has mentioned that HTTPS is a ranking signal. In addition, its generally good practice for all websites to use SSL to prevent hackers and third parties from intercepting requests and data.

Additionally, many modern site speed and performance techniques require a valid SSL/HTTPS setup. To take advantage of faster web technologies like HTTP/2 and the upcoming HTTP/3, browsers like Google Chrome and Firefox need the website to have a valid SSL certificate.

If you want to move to SSL and ensure that your site is served correctly over HTTPS, we have a handy guide with tips & tricks for moving to HTTPS. Since version 5.7, WordPress comes with a basic tool to make the detection of and move to HTTPS easier.

Its worth spending some time clicking through all of the sections in the WordPress Settings menu, as many of the options there can impact the SEO of your WordPress site.

In particular, its worth double-checking your visibility settings in Settings Reading, to make sure that youre not accidentally preventing search engines from indexing your website. Thatd definitely hurt your visibility and your WordPress SEO!

You should also make sure that your Writing and Reading settings are all set correctly, these control your default categories, and what should be displayed on your homepage. Dont forget to give your site a strong tagline in Settings General, too!

Your permalink settings define what format your page and post URLs will take, which can have a big impact on WordPress SEO. So if youre creating a new site, one of the first things you should do is change your permalink settings, which you can find in Settings Permalinks.

If you dont change your settings from the default, all of your pages and posts will have URLs that look like example.com/?p=123. Whilst this is perfectly okay, its not particularly nice, and it might impact how users and search engines perceive the quality and relevance of your pages.

Changing the permalink structure alters the components, ordering, and structure of your websites URLs. Therefore, its important to select the right structure when setting up your website, as changing it later can cause SEO issues.

We usually recommend that people use a structure that creates URLs that look like example.com/post-name/, or example.com/category/post-name/, depending on how much importance they anticipate placing on the categorization of their content. For most WordPress sites, choosing either of these options will be perfectly suitable.

For the first option, you can just change the permalink setting to /%postname%/, like so:

To include the category, you can select Custom Structure and change the value to /%category%/%postname%/.

If you previously had ?p= as your permalink, WordPress will take care of all the redirects for you. This is also true if you change from /%postname%/ to /%category%/%postname%/.

If you have an established site and change from any other permalink structure, you might want to consult our article on changing your WordPress permalink structure and the tool that youll find within it.

You need to think about what you want your site to show up as http://www.example.com, or simply example.com. Make sure that in your general settings, in Settings General, the version you want to show up is properly reflected:

From an SEO perspective, theres little difference either way. Additionally, most hosting and server setups will automatically redirect requests for the wrong version to the version youve selected. That makes this primarily a branding consideration which approach feels best for your site?

From a technical perspective, theres not a huge amount of difference, either. Some setups might have some minor headaches if they omit the www component, but these are increasingly rare.

WordPress SEO also means your site should provide the best content on your chosen subject period. People are looking for engaging, authoritative articles and trustworthy answers to their questions. Writing high-quality content for your WordPress site begins with your unique ideas or distinctive take on a particular topic. But it also means presenting these ideas in a well-structured and accessible manner. Together, this will help you attract the audience youre looking for and keep them engaged.

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Before writing your content, you should think about what search terms you want to be found for. Then, you should optimize every page or post for a specific keyphrase.

But how can you determine what keyphrase you want to be found for? To find out, you need to do keyword research. In this process, you should ask yourself questions: what terms do I want to rank for? How realistic is it that I can rank for these terms?

Imagine you have a baking blog, and youre passionate about sharing your favorite recipes and baking techniques. Optimizing a post for a term such as [best cake recipe] isnt a realistic goal because its a very general term. Theres a lot of competition for such general terms. Instead, it would help if you thought about finding your own niche. This niche could be [healthy, low-sugar cake recipes] or [French patisserie you can make at home].

Within a niche, you can become an expert. Your expertise enables you to create content that goes beyond that of your competitors. You can go deeper than others or shed light on different angles of the same topic. For this, youll want to focus on long-tail keyphrases. For example, a long-tail keyphrase might be [how to make a low-calorie vegan blueberry cheesecake]. A keyphrase like this is more specific, and therefore easier to rank for. Also, itll be more suitable for your particular niche topic.

Its also essential to think about what your audience wants to achieve by searching for a specific term. This is called search intent. For example, they could be looking for the answer to a particular question, and you can provide the necessary information. Or they might want to buy a specific product that you can offer them. Think about the needs of your visitors and address them by creating content accordingly.

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After youve done your keyword research and know the topics you want to write about, you need to get to the actual writing. Most of the time, thats easier said than done. To get from an idea to a great piece of content, most likely, youll have to follow a cycle of drafting, writing, editing, and rewriting.

Your first draft can be an outline of your structure. You dont have to write out everything in perfect prose at this point, but make sure that you follow a logical structure. Most pieces will include an introduction, your main points of argument, and a conclusion. Of course, this will vary per genre a recipe will have a completely different structure.

You can further flesh out the points in the writing phase, where you try to develop a first complete version of your text. Finally, you should check whether your piece is engaging and easy to read in the editing phase. You might be an expert on your topic, but your audience probably isnt (yet). So try to make your writing as accessible as possible. Readability is essential. The readability analysis in Yoast SEO helps you improve your writing.

When in doubt, its always best to ask a friend or colleague for some feedback. Another helpful trick is to read your text out loud to yourself. You can even let your computer speak it. It will give you a better idea of whether everything flows nicely.

When writing or editing your post for your WordPress site, you need to pay special attention to some elements to make it SEO-friendly. These elements include your subheadings, your title, and your meta description all of these need to reflect the topic of the specific post.

Dont forget, SEO-friendly doesnt just mean that its easy for a search engine to grasp a pages topic. More importantly, it means that your visitors can get the gist of your page at a single glance.

Your meta description and your title might be a deciding factor for whether visitors click on your page in the search results in the first place. And once theyve visited your site, elements like subheadings can be critical for visitors to decide whether they want to stay on your site.

One important rule is not to use a focus keyphrase on more than one page. Otherwise, you might end up cannibalizing yourself. Most of the time, you dont want to rank for multiple pages on the same keyphrase because it means that youre setting yourself up as your own competition.

Its also essential to include the focus keyphrase in crucial elements of your post, such as the title, the introduction, your subheadings, and your meta description. All of these elements are signals for what your post is about. Since your focus keyphrase is, in fact, the main topic of your page, its a logical consequence that you should make sure this topic is reflected in all of these elements.

The same logic holds for your text overall: you need to make sure that you dont stray off-topic; if you stay on-topic, it should follow naturally that you use your keyphrase multiple times throughout your text. But avoid stuffing your writing with your keyphrase just for the sake of it. If you find it hard to include your keyphrase in your text a sufficient number of times, it might be a sign that you should take a different approach to the topic.

To avoid repetition, you can use synonyms. Synonyms are words that mean the same or more or less the same as your keyphrase. An example of this is the words film and movie. Search engines will recognize that they have the same meaning, which you can also check by having a look at the search results: if you search for movie, film will also be highlighted in the results, and vice versa.

You can also make use of related keyphrases to optimize a single page for similar, related terms. You can use these to give context to your keyphrase. For example, if your keyphrase is [pumpkin soup], your related keyphrase might be [winter weeknight dinners]. This second, broader term gives additional information about your topic. It can also create coherence by establishing a link to similar pages on your post.

The Yoast SEO Premium analysis makes it easier to optimize your post thanks to word forms, synonyms, and related keyphrases. Not only that, but Yoast SEO also comes with a Semrush integration that helps you find related keyphrases including volume and trend data.

In most cases, your posts URL should probably contain your focus keyphrase, so that its obvious what your page is about from the link. That said, you should always try and keep your permalinks short, descriptive, and clean dont put unnecessary words in for the sake of it!

Before you publish new posts or pages, you may also wish to consider removing function words from your permalink. These are words like a, and, and the. When done carefully, this may make your permalinks more readable, and easier to use or link to. Posts with especially long titles may benefit from this approach.

For posts that you have already published, wed recommend being careful when changing permalinks. If people have already linked to your pages, changing the URLs may make a mess. Even though WordPress will sometimes redirect users to the new location (the redirect manager in Yoast SEO Premium handles this automatically and more reliably), changing URLs can impact performance.

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