Comprehensive Health Research Center - Research, Education, Training and Innovation in Clinical research and Public Health

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Decision-making support systems on extended hospital length of stay: validation and recalibration of a model for patients with AMI

Publication . Xavier, Joana; Seringa, Joana; Pinto, Fausto J.; Magalhães, Teresa

Background: Cardiovascular diseases are still a significant cause of death and hospitalization. In 2019, circulatory diseases were responsible for 29.9% of deaths in Portugal. These diseases have a significant impact on the hospital length of stay. Length of stay predictive models is an efficient way to aid decision-making in health. This study aimed to validate a predictive model on the extended length of stay in patients with acute myocardial infarction at the time of admission. Methods: An analysis was conducted to test and recalibrate a previously developed model in the prediction of prolonged length of stay, for a new set of population. The study was conducted based on administrative and laboratory data of patients admitted for acute myocardial infarction events from a public hospital in Portugal from 2013 to 2015. Results: Comparable performance measures were observed upon the validation and recalibration of the predictive model of extended length of stay. Comorbidities such as shock, diabetes with complications, dysrhythmia, pulmonary edema, and respiratory infections were the common variables found between the previous model and the validated and recalibrated model for acute myocardial infarction. Conclusion: Predictive models for the extended length of stay can be applied in clinical practice since they are recalibrated and modeled to the relevant population characteristics.

2023Journal article

Open access

Correlation between hyperglycemia and glycated albumin with retinopathy of prematurity

Publication . Almeida, Ana C.; Silva, Gabriela A.; Santini, Gabriele; Brízido, Margarida; Correia, Miguel; Coelho, Constança; Borrego, Luís Miguel

To determine the association between hyperglycemia, glycated albumin (GlyA) and retinopathy of prematurity (ROP). Prospective study of all infants under ROP screening from March 2017 to July 2019. All demographic, clinical and laboratory data were collected. Glucose was measured at birth and every 8 h for the first week and serum GlyA was evaluated at birth, 1st, 2nd and 4th weeks after birth. Reference range for GlyA was obtained. Univariate logistic regression was used to examine risk factors for ROP followed by multivariate regression. A total of 152 infants were included in the study. Median gestational age was 30 weeks and median birth weight 1240 g. Thirty-three infants (21.7%) had ROP. Hyperglycemia was present in 24 (72.7%) infants diagnosed with any ROP versus 6 (0.05%) in those without ROP. Median GlyA at birth, 1st, 2nd and 4th and respective reference ranges were 8.50% (6.00-12.65), 8.20% (5.32-11.67), 8.00% (5.32-10.00) and 7.90% (5.30-9.00) respectively. After multivariate logistic regression, hyperglycemia but not GlyA, remained a significant risk factor for ROP overpowering the other recognized risk factors (Exp (B) 28.062, 95% CI for Exp(B) 7.881-99.924 p < 0.001). In our cohort, hyperglycemia but not GlyA, remained a significant risk factor for ROP overpowering the other recognized risk factors.

2021Journal article

Open access

Clinical outcomes, complications and fusion rates in endoscopic assisted intraforaminal lumbar interbody fusion (iLIF) versus minimally invasive transforaminal lumbar interbody fusion (MI-TLIF): systematic review and meta-analysis

Publication . Sousa, José Miguel; Ribeiro, Hugo; Silva, João Luís; Nogueira, Paulo Jorge; Consciência, José Guimarães

This meta-analysis aims to determine the clinical outcomes, complications, and fusion rates in endoscopic assisted intra-foraminal lumbar interbody fusion (iLIF) and minimally invasive transforaminal lumbar interbody fusion (MI-TLIF) for lumbar degenerative diseases. The MEDLINE, Embase, and Cochrane Library databases were searched. The inclusion criteria were: five or more consecutive patients who underwent iLIF or MI-TLIF for lumbar degenerative diseases; description of the surgical technique; clinical outcome measures, complications and imaging assessment; minimum follow-up of 12 months. Surgical time, blood loss, and length of hospital stay were extracted. Mean outcome improvements were pooled and compared with minimal clinically important differences (MCID). Pooled and direct meta-analysis were evaluated. We identified 42 eligible studies. The iLIF group had significantly lower mean intra-operative blood loss, unstandardized mean difference (UMD) 110.61 mL (95%CI 70.43; 150.80; p value < 0.0001), and significantly decreased length of hospital stay (UMD 2.36; 95%CI 1.77; 2.94; p value < 0.0001). Visual analogue scale (VAS) back, VAS leg and Oswestry disability index (ODI) baseline to last follow-up mean improvements were statistically significant (p value < 0.0001), and clinically important for both groups (MCID VAS back > 1.16; MCID VAS leg > 1.36; MCID > 12.40). There was no significant difference in complication nor fusion rates between both cohorts. Interbody fusion using either iLIF or MI-TLIF leads to significant and clinically important improvements in clinical outcomes for lumbar degenerative diseases. Both procedures provide high rates of fusion at 12 months or later, without significant difference in complication rates. iLIF is associated with significantly less intraoperative blood loss and length of hospital stay.

2022Journal article

Open access

Machine learning prediction of mortality in acute myocardial infarction

Publication . Oliveira, Mariana; Seringa, Joana; Pinto, Fausto J.; Henriques, Roberto; Magalhães, Teresa

Background: Acute Myocardial Infarction (AMI) is the leading cause of death in Portugal and globally. The present investigation created a model based on machine learning for predictive analysis of mortality in patients with AMI upon admission, using different variables to analyse their impact on predictive models. Methods: Three experiments were built for mortality in AMI in a Portuguese hospital between 2013 and 2015 using various machine learning techniques. The three experiments differed in the number and type of variables used. We used a discharged patients' episodes database, including administrative data, laboratory data, and cardiac and physiologic test results, whose primary diagnosis was AMI. Results: Results show that for Experiment 1, Stochastic Gradient Descent was more suitable than the other classification models, with a classification accuracy of 80%, a recall of 77%, and a discriminatory capacity with an AUC of 79%. Adding new variables to the models increased AUC in Experiment 2 to 81% for the Support Vector Machine method. In Experiment 3, we obtained an AUC, in Stochastic Gradient Descent, of 88% and a recall of 80%. These results were obtained when applying feature selection and the SMOTE technique to overcome imbalanced data. Conclusions: Our results show that the introduction of new variables, namely laboratory data, impacts the performance of the methods, reinforcing the premise that no single approach is adapted to all situations regarding AMI mortality prediction. Instead, they must be selected, considering the context and the information available. Integrating Artificial Intelligence (AI) and machine learning with clinical decision-making can transform care, making clinical practice more efficient, faster, personalised, and effective. AI emerges as an alternative to traditional models since it has the potential to explore large amounts of information automatically and systematically.

2023Journal article

Open access

Preterm birth characteristics and outcomes in Portugal, between 2010 and 2018: a cross‐sectional sequential study

Publication . Elias, Cecília; Nogueira, Paulo Jorge; Sousa, Paulo

Introduction: According to the World Health Organization, 11% of all children are born prematurely, representing 15 million births annually. An extensive analysis on preterm birth, from extreme to late prematurity and associated deaths, has not been published. The authors characterize premature births in Portugal, between 2010 and 2018, according to gestational age, geographic distribution, month, multiple gestations, comorbidities, and outcomes. Methods: A sequential, cross-sectional, observational epidemiologic study was conducted, and data were collected from the Hospital Morbidity Database, an anonymous administrative database containing information on all hospitalizations in National Health Service hospitals in Portugal, and coded according to the ICD-9-CM (International Classification of Diseases), until 2016, and ICD-10 subsequently. Data from the National Institute of Statistics was utilized to compare the Portuguese population. Data were analyzed using R software. Results: In this 9-year study, 51.316 births were preterm, representing an overall prematurity rate of 7.7%. Under 29 weeks, birth rates varied between 5.5% and 7.6%, while births between 33 and 36 weeks varied between 76.9% and 81.0%. Urban districts presented the highest preterm rates. Multiple births were 8× more likely preterm and accounted for 37%-42% of all preterm births. Preterm birth rates slightly increased in February, July, August, and October. Overall, respiratory distress syndrome (RDS), sepsis, and intraventricular hemorrhage were the most common morbidities. Preterm mortality rates varied significantly with gestational age. Conclusion: In Portugal, 1 in 13 babies was born prematurely. Prematurity was more common in predominantly urban districts, a surprise finding that warrants further studies. Seasonal preterm variation rates also require further analysis and modelling to factor in heat waves and low temperatures. A decrease in the case rate of RDS and sepsis was observed. Compared with previously published results, preterm mortality per gestational age decreased; however, further improvements are attainable in comparison with other countries.

2023Journal article

Open access