Confounders contributing to explain the association between sex and disease impact in patients with recent-onset psoriatic arthritis

OBJECTIVES

To evaluate the effect of potential confounders on the association between sex and disease impact in recent-onset psoriatic arthritis.

METHODS

We performed a multicentre observational prospective study (2-year follow-up, regular annual visits). The study population comprised patients aged ≥18 years who fulfilled the CASPAR criteria and less than 2 years since the onset of symptoms. The dataset was generated using data for each patient at the 3 visits (baseline, first year, and second year of follow-up) matched with the PsAID values at each of the 3 visits. Once variables associated with both PsAID ≥4 and sex were selected, those that led to a difference of >10% between the adjusted and crude estimations were identified as potential confounders in the association between sex and PsAID. Lastly, the final multivariate logistic regression model estimating the association between sex and PsAID was defined.

RESULTS

The dataset contained 418 observations (158 at baseline, 135 at the first follow-up visit, and 125 at the second visit). The confounders identified in the multivariate model were HAQ, global pain, level of physical activity, and joint pattern at diagnosis. After adjustment for these variables, no statistically significant association was observed between female sex and PsAID ≥4.

CONCLUSIONS

The association between female sex and greater disease impact could be explained by the influence of other variables, specifically higher HAQ score, greater intensity of pain, differences in the level of physical activity and in the joint pattern at diagnosis (lower frequency of the spondylitis pattern in women).

Characteristics associated with the perception of high-impact disease (PsAID ≥4) in patients with recent-onset psoriatic arthritis. Machine learning-based model

OBJECTIVES

To evaluate which patient and disease characteristics are associated with the perception of high-impact disease (PsAID ≥4) in recent-onset psoriatic arthritis.

METHODS

We performed a multicenter observational prospective study (2-year follow-up, regular annual visits). The study population comprised patients aged ≥18 years who fulfilled the CASPAR criteria and less than 2 years since the onset of symptoms. The dataset was generated using data for each patient at the 3 visits (baseline, first year, and second year of follow-up) matched with the PsAID values at each of the 3 visits. PsAID was categorized into two groups (<4 and ≥4). We trained a logistic regression model and random forest-type and XGBoost machine learning algorithms to analyze the association between the outcome measure and the variables selected in the bivariate analysis. A k-fold cross-validation with k = 5 was performed.

RESULTS

The sample comprised 158 patients. Of the patients who attended the clinic, 45.8% scored PsAID ≥4 at baseline; 27.1%, at the first follow-up visit, and in 23.0%, at the second follow-up visit. The variables associated with PsAID ≥4 were, in decreasing order of importance: HAQ, pain, educational level, and physical activity. Higher HAQ (logistic regression coefficient 10.394; IC95% 7.777,13.011), higher pain (5.668; 4.016, 7.320), lower educational level (-2.064; -3.515, -0.613) and high level of physical activity (1.221; 0.158, 2.283) were associated with a higher frequency of PsAID ≥4. The mean values of the measures of validity of the algorithms were all ≥85%.

CONCLUSIONS

Despite the higher weight given to pain when scoring PsAID, we observed a greater influence of physical function on disease impact.

Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis: predictive model based on machine learning

Background

Very few data are available on predictors of minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis (PsA). Such data are crucial, since the therapeutic measures used to change the adverse course of PsA are more likely to succeed if we intervene early. In the present study, we used predictive models based on machine learning to detect variables associated with achieving MDA in patients with recent-onset PsA.

Methods

We performed a multicenter observational prospective study (2-year follow-up, regular annual visits). The study population comprised patients aged ≥18 years who fulfilled the CASPAR criteria and less than 2 years since the onset of symptoms. The dataset contained data for the independent variables from the baseline visit and from follow-up visit number 1. These were matched with the outcome measures from follow-up visits 1 and 2, respectively. We trained a random forest–type machine learning algorithm to analyze the association between the outcome measure and the variables selected in the bivariate analysis. In order to understand how the model uses the variables to make its predictions, we applied the SHAP technique. We used a confusion matrix to visualize the performance of the model.

Results

The sample comprised 158 patients. 55.5% and 58.3% of the patients had MDA at the first and second follow-up visit, respectively. In our model, the variables with the greatest predictive ability were global pain, impact of the disease (PsAID), patient global assessment of disease, and physical function (HAQ-Disability Index). The percentage of hits in the confusion matrix was 85.94%.

Conclusions

A key objective in the management of PsA should be control of pain, which is not always associated with inflammatory burden, and the establishment of measures to better control the various domains of PsA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-022-02838-2.

AB0931 Characteristics associated with the perception of high-impact disease (PsAID ≥4) in patients with recent-onset psoriatic arthritis. Model based on machine learning

Background

The Psoriatic Arthritis Impact of Disease (PsAID) questionnaire is the standard tool for evaluating the impact of psoriatic arthritis (PsA) on quality of life [1]. Variables associated with high disease impact were studied in patients with long-standing established disease. The characteristics associated with high-impact PsAID in recent-onset PsA remain unknown.

Objectives

To evaluate which patient and disease characteristics are associated with the perception of high-impact disease (PsAID ≥4) in recent-onset PsA.

All patients gave their informed consent. The study was approved by the Clinical Research Ethics Committee of the Principality of Asturias.

We conducted a cross-sectional analysis. The dataset was generated using data for the independent variables at the 3 visits (baseline, first year, and second year of follow-up) matched with the PsAID values at each of the 3 visits. PsAID was categorized into two groups, namely, <4 and ≥4 [1]. We trained logistic regression models and a random forest–type machine learning algorithm to analyze the association between the outcome measure and the variables selected in the bivariate analysis (statistical significance defined as p value <0.05). We used a confusion matrix to visualize the performance of the final model. This matrix shows the real class of the data items, together with the class predicted by the machine learning algorithm, and records the number of hits and misses.

Results

The sample comprised 158 patients. 20.9% were lost to follow-up. Of the patients who attended the clinic, 45.8% scored PsAID ≥4 at baseline; 27.1%, at the first follow-up visit, and 23.0%, at the second follow-up visit. The variables associated with PsAID ≥4 selected in the logistic regression analysis were HAQ, patient global pain during the previous week, educational level, and level of physical activity in the previous week. The association was positive for the first 2 variables and for level of physical activity and negative for educational level. When physical activity was introduced as a categorical variable, a possible negative association was observed for a moderate level (although this was not statistically significant) and a positive association was observed for a high level (Table 1).

Table 1.

Variables associated with PsAID ≥4: Logistic regression analysis.

VariableRegression coefficient95% CIp value (Wald test)HAQ10.394[7.777, 13.011]<0.001Patient global pain in the previous week5.668[4.016, 7.320]<0.001Educational level-2.064[-3.515, -0.613]0.005Moderate level of physical activity in the previous week-0.341[-1.255, 0.573]0.465High level of physical activity in the previous week1.221[0.158, 2.283]0.024

When the random forest–type machine learning algorithm was trained with these 4 variables, the order of importance (from more to less) attributed by the model was: patient global pain, HAQ, educational level, and physical activity. The percentage of hits in the confusion matrix was 86.14%.

Conclusion

Pain control and control of the disease as a whole, preventing patients from suffering a decrease in their functional capacity, are first-order treatment objectives. PsA patients should take regular physical exercise, but with a moderate or low impact on their joints and entheses.

References

[1]Orbai A, Holland R, Leung YY, Tillett W, Goel N, McHugh N et al. PsAID12 provisionally endorsed at OMERACT 2018 as core outcome measure to assess psoriatic arthritis-specific health- related quality of life in clinical trials. J Rheumatol 2019;46:990–95.

Acknowledgements

The authors would like to acknowledge José Luis Fernández Sueiro for his contribution to the conception of the study; José Miguel Carrasco for his contribution to the design of the study; Nuria Montero and Cristina Oliva for her contribution to data monitoring; Ana González Marcos and Cristina Pruenza for her contribution to data analysis; Thomas O´Boyle for the translation of the manuscript; and non-author investigators of Proyecto REAPSER Study Group

Disclosure of Interests

None declared

POS1074 MINIMAL DISEASE ACTIVITY (MDA) IN PATIENTS WITH RECENT-ONSET PSORIATIC ARTHRITIS. PREDICTIVE MODEL BASED ON MACHINE LEARNING

Background

Very few data are available on predictors of minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis (PsA). Such data are crucial, since the therapeutic measures used to change the adverse course of PsA are more likely to succeed if we intervene early.

Objectives

To detect patient and disease variables associated with achieving MDA in patients with recent-onset PsA.

Methods

We performed a multicenter observational prospective study (2-year follow-up, regular annual visits), promoted by the Spanish Society of Rheumatology. Patients aged ≥18 years who fulfilled the CASPAR criteria, with less than 2 years since the onset of symptoms, were included. The intention at the baseline visit was to reflect the patient’s situation before disease progress was modified by the treatments prescribed by the rheumatologist.

All patients gave their informed consent. The study was approved by the Clinical Research Ethics Committee of the Principality of Asturias.

MDA was defined as fulfillment of at least 5 of the following: ≤1 tender joint; ≤1 swollen joint; PASI ≤1 or BSA ≤3%; score on the visual analog scale (VAS) for pain provided by the patient ≤1.5; overall score for disease activity provided by the patient ≤2; HAQ score ≤0.5; ≤1 painful enthesis [1].

The dataset contained data for the independent variables from the baseline visit and from follow-up visit number 1. These were matched with the outcome measures from follow-up visits 1 and 2, respectively. We trained a random forest–type machine learning algorithm to analyze the association between the outcome measure and the variables selected in the bivariate analysis. In order to understand how the model uses the variables to make its predictions, we applied the SHAP technique. This approach assigns a SHAP value to each value of each variable according to the extent to which it affects the prediction of the model (the higher the absolute SHAP value, the greater the influence of this data item on prediction) and to how it affects the prediction (if the SHAP value is positive, the data item positively affects the prediction, that is, it confers a higher value on the prediction). The SHAP summary graphs order the predictors by their importance in the predictions of the model. This importance is calculated with the mean of the SHAP values assigned to each data item of a variable; mean values <0.01 indicate the low importance of the variable in the model. We used a confusion matrix to visualize the performance of the model. This matrix shows the real class of the data items, together with the predicted class, and records the number of hits and misses.

Results

The sample comprised 158 patients. 14.6% were lost to follow-up. 55.5% and 58.3% of the patients had MDA at the first and second follow-up visit, respectively. The importance of the variables in the model according to the mean of the SHAP values is shown in Table 1. The variables with the greatest predictive ability were global pain, impact of the disease (PsAID), patient global assessment of disease and physical function (HAQ-Disability Index). The SHAP values for each value of each variable are shown in Figure 1. The percentage of hits in the confusion matrix was 85.94%.

Table 1.

Variables in the predictions of the random forest for MDA according to the SHAP method.

VariableImportance according to SHAP1Global pain0.069PsAID0.064Patient global assessment of disease0.047HAQ0.044Articular pattern at diagnosis0.029Physician global assessment of disease0.023Tender joint count0.014Sex0.009Weekly alcohol consumption0.009

1Mean of the SHAP values for each value of the variable.

MDA: minimal disease activity.

Figure 1.

SHAP summary graph.

Conclusion

A key objective in the management of PsA should be control of pain, which is not always associated with inflammatory burden, and the establishment of measures to better control the various domains of PsA.

References

[1]Coates LC, Fransen J, Helliwell PS. Defining minimal disease activity in psoriatic arthritis: a proposed objective target for treatment. Ann Rheum Dis. 2010;69:48-53.

Acknowledgements

The authors would like to acknowledge José Luis Fernández Sueiro for the conception of the study; José Miguel Carrasco for his contribution to the design of the study; Nuria Montero and Cristina Oliva for her contribution to data monitoring; Ana González Marcos and Cristina Pruenza for her contribution to data analysis; and Thomas O´Boyle for the translation of the manuscript.

Disclosure of Interests

None declared

Severe Disease in Patients With Recent-Onset Psoriatic Arthritis. Prediction Model Based on Machine Learning

Objectives

To identify patient- and disease-related characteristics that make it possible to predict higher disease severity in recent-onset PsA.

Methods

We performed a multicenter observational prospective study (2-year follow-up, regular annual visits). The study population comprised patients aged ≥ 18 years who fulfilled the CASPAR criteria and less than 2 years since the onset of symptoms. Severe disease was defined at each visit as fulfillment of at least 1 of the following criteria: need for systemic treatment, Health Assessment Questionnaire (HAQ) > 0.5, polyarthritis. The dataset contained data for the independent variables from the baseline visit and follow-up visit number 1. These were matched with the outcome measures from follow-up visits 1 and 2, respectively. We trained a logistic regression model and random forest-type and XGBoost machine learning algorithms to analyze the association between the outcome measure and the variables selected in the bivariate analysis.

Results

The sample comprised 158 patients. At the first follow-up visit, 78.2% of the patients who attended the clinic had severe disease. This percentage decreased to 76.4% at the second visit. The variables predicting severe disease were patient global pain, treatment with synthetic DMARDs, clinical form at diagnosis, high CRP, arterial hypertension, and psoriasis affecting the gluteal cleft and/or perianal area. The mean values of the measures of validity of the machine learning algorithms were all ≥ 80%.

Conclusion

Our prediction model of severe disease advocates rigorous control of pain and inflammation, also addressing cardiometabolic comorbidities, in addition to actively searching for hidden psoriasis.

Life span history of non-fatal suicidal behaviours in a large sample of general practitioners' patients: data from Rovigo, Northern Italy

A survey about history of non-fatal suicidal behaviour was performed on 1,171 subjects in the waiting room of general practitioners' practices in the territory of Rovigo (Northern Italy). The mean age of interviewed subjects was 52.9 ± 17.0, with a majority of female individuals. Two and two percent admitted previous experience of non-suicidal self-injury, 4.7 % admitted having had serious suicidal thoughts/plans, and 1.8 % reported at least one suicide attempt. Compared to the rest of the sample, people with history of suicidal behaviours resulted to be of younger age (p < .05), whilst their level of well-being was poorer (p < .001). When compared to the results of the Italian arm of the European Study of the Epidemiology of Mental Disorders, carried out on general population samples, the present study produces higher rates of suicidality, despite the much higher mean age of the interviewed subjects compared to the general population.

Parasuicide in Rovigo (North of Italy) during the period 2000-2005

INTRODUCTION

The greatest predictor of eventual suicide is parasuicide, which includes both suicide attempts and deliberate self-harm with no intent to die. The rate of parasuicide is reckoned to be at least ten times the suicide rate.

METHODS

An observational study of the population of Rovigo Public Health Unit has been carried out to investigate parasuicide cases that presented to a general hospital in the six-year period from 1st January 2000 to 31st December 2005.

RESULTS

An incidence of 36.39 parasuicides/100,000/year referred to a general hospital has been estimated, with a majority of female and young subjects. The principal method used was drug poisoning (59.1%); the more frequent diagnoses are mood and personality disorders. Method of attempt distribution is different for age and gender (p < 0.001), while diagnosis distribution is different only for age (p < 0.001). Comparison between method of attempt and diagnosis distribution indicates a significant difference (p < 0.01). In 36.4% of cases there was no contact with the Mental Health Service after parasuicide.

DISCUSSION

The present study confirms that parasuicide is more common in females and younger people and that the more probable diagnoses are mood and personality disorders. The finding of a high number of subjects without any previous contact with the Mental Health Service and, especially, after parasuicide, claims attention on primary and secondary prevention of suicidal behaviour.

CONCLUSIONS

The results appear to be in line with those from literature on parasuicide in Western populations.

Sex hormones and trace elements in rat CCL4-induced cirrhosis and hepatocellular carcinoma

Several biochemical events accompany and mediate the development of chronic liver disease and its evolution into cancer. Low plasma zinc and high copper levels have been observed in various liver diseases, such as liver cirrhosis and viral hepatitis, while increased oestradiol levels have been documented in chronic liver damage and hepatocellular carcinoma. We administered CCL4 intragastrically to 10 female Sprague Dawley rats for 30 weeks. All animals developed cirrhosis and four also developed hepatocellular carcinoma. Plasma levels of zinc, copper and oestradiol were significantly higher in the latter group than in animals with simple cirrhosis. Progesterone, AST and bilirubin showed a trend toward significant differences whereas testosterone and ALP levels were unchanged. These findings add to the evidence that sex hormones and trace elements are involved in the process of the development of chronic liver damage and carcinogenesis.