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FEDERICO MOTTA

Dottorando
Dipartimento di Scienze Fisiche, Informatiche e Matematiche

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Pubblicazioni

2023 - A Machine Learning Approach to Predict Weight Change in ART-Experienced People Living with HIV [Articolo su rivista]
Motta, F.; Milic, J.; Gozzi, L.; Belli, M.; Sighinolfi, L.; Cuomo, G.; Carli, F.; Dolci, G.; Iadisernia, V.; Burastero, G.; Mussini, C.; Missier, P.; Mandreoli, F.; Guaraldi, G.
abstract

Introduction:The objective of the study was to develop machine learning (ML) models that predict the percentage weight change in each interval of time in antiretroviral therapy-experienced people living with HIV.Methods:This was an observational study that comprised consecutive people living with HIV attending Modena HIV Metabolic Clinic with at least 2 visits. Data were partitioned in an 80/20 training/test set to generate 10 progressively parsimonious predictive ML models. Weight gain was defined as any weight change >5%, at the next visit. SHapley Additive exPlanations values were used to quantify the positive or negative impact of any single variable included in each model on the predicted weight changes.Results:A total of 3,321 patients generated 18,322 observations. At the last observation, the median age was 50 years and 69% patients were male. Model 1 (the only 1 including body composition assessed with dual-energy x-ray absorptiometry) had an accuracy greater than 90%. This model could predict weight at the next visit with an error of <5%.Conclusions:ML models with the inclusion of body composition and metabolic and endocrinological variables had an excellent performance. The parsimonious models available in standard clinical evaluation are insufficient to obtain reliable prediction, but are good enough to predict who will not experience weight gain.

2023 - Bone Mineral Density and Trabecular Bone Score Changes throughout Menopause in Women with HIV [Articolo su rivista]
Milic, Jovana; Renzetti, Stefano; Morini, Denise; Motta, Federico; Carli, Federica; Menozzi, Marianna; Cuomo, Gianluca; Mancini, Giuseppe; Simion, Mattia; Romani, Federico; Spadoni, Anna; Baldisserotto, Irene; Barp, Nicole; Diazzi, Chiara; Mussi, Chiara; Mussini, Cristina; Rochira, Vincenzo; Calza, Stefano; Guaraldi, Giovanni
abstract

Objective: The objectives of this study were to describe the trajectories of bone mineral density (BMD) and trabecular bone score (TBS) changes throughout pre-menopause (reproductive phase and menopausal transition) and post-menopause (early and late menopause) in women with HIV (WWH) undergoing different antiretroviral therapies (ARTs) and explore the risk factors associated with those changes. Methods: This was an observational longitudinal retrospective study in WWH with a minimum of two DEXA evaluations comprising BMD and TBS measurements, both in the pre-menopausal and post-menopausal periods. Menopause was determined according to the STRAW+10 criteria, comprising four periods: the reproductive period, menopausal transition, and early- and late-menopausal periods. Mixed-effects models were fitted to estimate the trajectories of the two outcomes (BMD and TBS) over time. Annualized lumbar BMD and TBS absolute and percentage changes were calculated in each STRAW+10 time window. A backward elimination procedure was applied to obtain the final model, including the predictors that affected the trajectories of BMD or TBS over time. Results: A total of 202 WWH, all Caucasian, were included. In detail, 1954 BMD and 195 TBS data were analyzed. The median number of DEXA evaluations per woman was 10 (IQR: 7, 12). The median observation periods per patient were 12.0 years (IQR = 8.9-14.4) for BMD and 6.0 years (IQR: 4.3, 7.9) for TBS. The prevalence of osteopenia (63% vs. 76%; p < 0.001) and osteoporosis (16% vs. 36%; p < 0.001) increased significantly between the pre-menopausal and post-menopausal periods. Both BMD (1.03 (±0.14) vs. 0.92 (±0.12) g/cm2; p < 0.001) and TBS (1.41 (IQR: 1.35, 1.45) vs. 1.32 (IQR: 1.28, 1.39); p < 0.001) decreased significantly between the two periods. The trend in BMD decreased across the four STRAW+10 periods, with a slight attenuation only in the late-menopausal period when compared with the other intervals. The TBS slope did not significantly change throughout menopause. The delta mean values of TBS in WWH were lower between the menopausal transition and reproductive period compared with the difference between menopause and menopausal transition. Conclusions: Both BMD and TBS significantly decreased over time. The slope of the change in BMD and TBS significantly decreased in the menopausal transition, suggesting that this period should be considered by clinicians as a key time during which to assess bone health and modifiable risk factors in WWH.

2023 - Quality of life and intrinsic capacity in patients with post-acute COVID-19 syndrome is in relation to frailty and resilience phenotypes. [Articolo su rivista]
Guaraldi, Giovanni; Milic, Jovana; Barbieri, Sara; Marchio', Tommaso; Caselgrandi, Agnese; Motta, Federico; Beghe', Bianca; Verduri, Alessia; Belli, Michela; Gozzi, Licia; Iadisernia, Vittorio; Faltoni, Matteo; Burastero, Giulia; Dessilani, Andrea; DEL MONTE, Martina; Dolci, Giovanni; Bacca, Erica; Franceschi, Giacomo; Yaacoub, Dina; Volpi, Sara; Mazzochi, Alice; Clini, Enrico; Mussini, Cristina
abstract

Background- The objective of this study was to characterize frailty and resilience in people evaluated for Post-Acute COVID-19 Syndrome (PACS), in relation to quality of life (QoL) and Intrinsic Capacity (IC). Methods- This cross-sectional, observational, study included consecutive people previously hospitalized for severe COVID-19 pneumonia attending Modena (Italy) PACS Clinic from July 2020 to April 2021. Four frailty-resilience phenotypes were built: “fit/resilient”, “fit/non-resilient”, “frail/resilient” and “frail/non-resilient”. Frailty and resilience were defined according to frailty phenotype and Connor Davidson resilience scale (CD-RISC-25) respectively. Study outcomes were: QoL assessed by means of Symptoms Short form health survey (SF-36) and health-related quality of life (EQ-5D-5L) and IC by means of a dedicated questionnaire. Their predictors including frailty-resilience phenotypes were explored in logistic regressions. Results- 232 patients were evaluated, median age was 58.0 years. PACS was diagnosed in 173 (74.6%) patients. Scarce resilience was documented in 114 (49.1%) and frailty in 72 (31.0%) individuals. Predictors for SF-36 score <61.60 were the phenotypes “frail/non-resilient” (OR=4.69, CI:2.08-10.55), “fit/non-resilient” (OR=2.79, CI:1.00-7.73). Predictors for EQ-5D-5L <89.7% were the phenotypes “frail/non-resilient” (OR=5.93, CI: 2.64-13.33) and “frail/resilient” (OR=5.66, CI:1.93-16.54). Predictors of impaired IC (below the mean score value) were “frail/non-resilient” (OR=7.39, CI:3.20-17.07), and “fit/non-resilient” (OR=4.34, CI:2.16-8.71) phenotypes. Conclusions- Resilience is complementary to frailty in the identification of clinical phenotypes with different impact on wellness and QoL. Frailty and resilience should be evaluated in hospitalized COVID-19 patients to identify vulnerable individuals to prioritize urgent health interventions in people with PACS.

2023 - Sarcopenic Obesity Phenotypes in Patients With HIV: Implications for Cardiovascular Prevention and Rehabilitation [Articolo su rivista]
Milic, Jovana; Calza, Stefano; Cantergiani, Samuele; Albertini, Maddalena; Gallerani, Altea; Menozzi, Marianna; Barp, Nicole; Todisco, Vera; Renzetti, Stefano; Motta, Federico; Mussini, Cristina; Sebastiani, Giada; Raggi, Paolo; Guaraldi, Giovanni
abstract

Background: To describe prevalence, incidence and risk factors for sarcopenic obesity (SO) phenotypes in people living with HIV (PWH) and their association with subclinical cardiovascular disease (CVD). Methods: Observational, longitudinal study of PWH. A minimum of one criterion was necessary to diagnose sarcopenia: (i) weak hand grip (HG), (ii) low appendicular skeletal muscle index (ASMI), (iii) short physical performance battery (SPPB <11). Obesity was defined as (i) body mass index (BMI) ≥30 kg/m2 or (ii) visceral adipose tissue (VAT) ≥160 cm2. These variables combined generated five SO phenotypes: (i) severe SO: low HG+ low ASMI + low SPPB + high BMI; (ii) SO1: weak HG + high VAT; (iii) SO2: weak HG + high BMI; (iv) SO3: low ASMI + high VAT; (v) SO4: low ASMI + high BMI. Subclinical CVD was defined as carotid intima media thickness (IMT) ≥1 mm, presence of carotid plaque, or CAC score >10. Results: Among 2379 PWH 72% men, median age was 52 years, median HIV vintage 21 years, and median BMI 24 kg/m2. Two PWH had severe SO. The prevalence of SO1-SO4 was 19.7%, 3.6%, 20.8% and 0.8% respectively. Incidence of SO1-SO4 was 6.90, 1.2, 5.6 and 0.29 x 100 persons-year, respectively. SO1 was associated with risk of IMT ≥ 1, and SO3 with risk of CAC score >10. Conclusions: There was a large variability in incidence and prevalence of SO phenotypes. The presence of SO may have important implications for cardiovascular prevention and cardiac rehabilitation of PWH who suffered an event.

2022 - A Machine learning approach to predict Weight change in ART experienced PLWH [Esposizione]
Motta, Federico; Milić, Jovana; Barbieri, Sara; Gozzi, Licia; Aprile, Emanuele; Belli, Michela; Venuta, Maria; Cuomo, Gianluca; Carli, Federica; Dolci, Giovanni; Iadisernia, Vittorio; Burastero, Giulia; Mussini, Cristina; Mandreoli, Federica; Guaraldi, Giovanni
abstract

2022 - Data-driven, AI-based clinical practice: experiences, challenges, and research directions [Relazione in Atti di Convegno]
Ferrari, Davide; Mandreoli, Federica; Motta, Federico; Missier, Paolo
abstract

Clinical practice is evolving rapidly, away from the traditional but inefficient detect-and-cure approach, and towards a Preventive, Predictive, Personalised and Participative (P4) vision that focuses on extending people’s wellness state. This vision is increasingly data-driven, AI-based, and is underpinned by many forms of "Big Health Data" including periodic clinical assessments and electronic health records, but also using new forms of self-assessment, such as mobile-based questionnaires and personal wearable devices. Over the last few years, we have been conducting a fruitful research collaboration with the Infectious Disease Clinic of the University Hospital of Modena having the main aim of exploring specific opportunities offered by data-driven AI-based approaches to support diagnosis, hospital organization and clinical research. Drawing from this experience, in this paper we provide an overview of the main research challenges that need to be addressed to design and implement data-driven healthcare applications. We present concrete instantiations of these challenges in three real-world use cases and summarise the specific solutions we devised to address them and, finally, we propose a research agenda that outlines the future of research in this field.

2022 - Decay pattern of anti–SARS–CoV–2 antibodies in PWH [Abstract in Atti di Convegno]
Milić, Jovana; Tili, Alessandro; Renzetti, Stefano; Motta, Federico; Meschiari, Marianna; Fogliani, Rossella; Ferrari, Filippo; Meccugni, Barbara; Mimmi, Stefano; Borsari, Silvana; Calza, Stefano; Cossarizza, Andrea; Mussini, Cristina; Guaraldi, Giovanni
abstract

2022 - From NAFLD to MAFLD: implications of change in terminology in PWH [Abstract in Atti di Convegno]
Guaraldi, Giovanni; Milić, Jovana; Renzetti, Stefano; Motta, Federico; Gozzi, Licia; Cervo, Adriana; Burastero, Giulia; Iadisernia, Vittorio; Lebouché, Bertrand; Al Hinai, Shaima; Deschenes, Marc; Raggi, Paolo; Calza, Stefano; Mussini, Cristina; Sebastiani, Giada
abstract

2022 - From NAFLD to MAFLD: implications of change in terminology in PWH [Abstract in Atti di Convegno]
Gozzi, Licia; Milić, Jovana; Renzetti, Stefano; Motta, Federico; Cervo, Adriana; Burastero, Giulia; Iadisernia, Vittorio; Lebouche, Bertrand; Al Hinai, Shaima; Deschenes, Marc; Menozzi, Marianna; Raggi, Paolo; Calza, Stefano; Mussini, Cristina; Sebastiani, Giada; Guaraldi, Giovanni
abstract

2022 - Machine learning algorithm to predict >5% Weight Gain in PWH switching to InSTI [Abstract in Atti di Convegno]
Guaraldi, Giovanni; Motta, Federico; Milić, Jovana; Barbieri, Sara; Gozzi, Licia; Aprile, Emanuele; Belli, Michela; Venuta, Maria; Cuomo, Gianluca; Carli, Federica; Dolci, Giovanni; Iadisernia, Vittorio; Burastero, Giulia; Mussini, Cristina; Mandreoli, Federica
abstract

2022 - Machine learning algorithm to predict >5% weight gain in PWH switching to INSTI [Poster]
Guaraldi, Giovanni; Motta, Federico; Milić, Jovana; Barbieri, Sara; Gozzi, Licia; Aprile, Emanuele; Belli, Michela; Venuta, Maria; Cuomo, Gianluca; Carli, Federica; Dolci, Giovanni; Iadisernia, Vittorio; Burastero, Giulia; Mussini, Cristina; Mandreoli, Federica
abstract

Background: Weight gain (WG) is a well-described phenomenon in PWH starting or switching ART. Machine learning (ML) methods is a tool of P4 medicine (Predictive, Preventive, Personalized & Participatory) and can generate models to identify patients at risk of WG. The objective was to develop an ML algorithm that predicts a 9-month WG≥5% in PLWH switching to InSTI with/without TAF. Methods: This was an observational study that comprised ART-experienced PWH attending Modena HIV metabolic clinic from 2004 to 2020. The patients' medical, HIV and ART data were partitioned in an 80/20 training/test set to generate predictive models. A ML model was used to leverage a hybrid approach where clinical expertise is applied along with data-driven analysis. The study outcome was the prediction at 9 months of weight change with a cut of 5%: at any patient visit (model 1) and in the subset of PWH switching to InSTI with/without TAF (model 2). 9-month prediction was chosen as being the minimum time occurring between any two given visits in the 95% of the cases. A robust implementation of linear regressor algorithms were able to predict weight gain/loss while tolerating missing data. Intelligible explanations were obtained through Shapley Additive exPlanations values (SHAP), which quantified the positive or negative impact of each variable included in each model on the predicted outcome. A measure of effectiveness (E-measure) was chosen as a performance metric, because unlike accuracy it can penalize errors, particularly underestimation ones. Results: A total of 2817 patients contributed to generate 10877 observations, which allowed construction of 2 predictive models based on 44-variables including anthropometric, HIV and laboratory biomarkers. At last observation median age was 51 years (IQR 11); 70% were male. Median CD4 nadir was 200 cells/μL (IQR 217), current CD4 was 659 cells/μL (IQR 372), 97% had undetectable VL and time since HIV diagnosis was 20 years (IQR 13). Median BMI was 23.4 (IQR 4.5) and 5.8% had obesity. The highest ranked variables used to train the models were weight at time of prediction and the ones depicted in the figure. Model 1 had accuracy of 84.4% and 83.9% E-measure; model 2 had accuracy of 84.4% and 86.4% E-measure. Conclusion: We developed a ML tool with a remarkable E-measure that may assist clinicians in decision-making and shift HIV care towards a P4 medicine. Immune-metabolic variables were more relevant than ART switching in the prediction of WG.

2022 - Machine learning algorithm to predict weight change in ART experienced PWH [Abstract in Atti di Convegno]
Motta, Federico; Milić, Jovana; Barbieri, Sara; Gozzi, Licia; Aprile, Emanuele; Belli, Michela; Venuta, Maria; Cuomo, Gianluca; Carli, Federica; Dolci, Giovanni; Iadisernia, Vittorio; Burastero, Giulia; Mussini, Cristina; Mandreoli, Federica; Guaraldi, Giovanni
abstract

2022 - Non-alcoholic to metabolic associated fatty liver disease: Cardiovascular implications of a change in terminology in patients living with HIV [Abstract in Atti di Convegno]
Raggi, Paolo; Milić, Jovana; Renzetti, Stefano; Motta, Federico; Gozzi, Licia; Cervo, Adriana; Burastero, Giulia; Iadisernia, Vittorio; Franceschi, Giacomo; Faltoni, Matteo; Mussini, Cristina; Sebastiani, Giada; Calza, Stefano; Guaraldi, Giovanni
abstract

Background and Aims: It has recently been suggested that the definition of non-alcoholic fatty liver disease (NAFLD) be changed to Metabolic Associated FLD (MAFLD) to better reflect the complex metabolic aspects of this syndrome. We compared the ability of MAFLD and NAFLD to correctly identify high CV risk patients, sub-clinical atherosclerosis or a history of prior CV events (CVEs) in patients living with HIV (PWH). Methods: Single center, cross-sectional study of PWH on stable anti-retrovirals. NAFLD was diagnosed by transient liver elastography; published criteria were used to diagnose MAFLD (JHepatol.2020;73(1):202-209). Four mutually exclusive groups were considered: low (<7.5%) vs high (>7.5%) ASCVD risk, subclinical CVD (carotid IMT ≥1 mm and/or coronary calcium score >100), and prior CVEs. The association of NAFLD and MAFLD with the CVD risk groups was explored via a multinominal model adjusted for age, sex, liver fibrosis, HIV duration, nadir CD4 and current CD4 cell count. Results: We included 1249 PWH (mean age 55 years, 74% men, median HIV duration 24 years). Prevalence of overweight/obesity and diabetes was 40% and 18%. Prevalence of NAFLD and MAFLD and overlapping groups are shown in Fig 1A. Fig 1B shows distribution of NAFLD/MAFLD in the 4 patient categories (p-for-trend <0.001). Both MAFLD and NAFLD were significantly associated with an increased risk of CVD compared to the reference level (ASCVD<7.5%) (all p-values <0.004; Fig 2). Conclusions: NAFLD and MAFLD perform equally in detecting CVD or its risk. The proposed change in terminology may not help to identify PWH requiring enhanced surveillance and preventative interventions for cardiovascular disease.

2022 - Non–alcoholic to metabolic associated fatty liver disease: cardiovascular implications of a change in terminology in patients living with HIV [Abstract in Atti di Convegno]
Milić, Jovana; Renzetti, Stefano; Motta, Federico; Gozzi, Licia; Cervo, Adriana; Burastero, Giulia; Iadisernia, Vittorio; Franceschi, Giacomo; Faltoni, Matteo; Volpi, Sara; Mazzocchi, Alice; Mussini, Cristina; Sebastiani, Giada; Calza, Stefano; Raggi, Paolo; Guaraldi, Giovanni
abstract

2022 - Real-world data mining meets clinical practice: Research challenges and perspective [Articolo su rivista]
Mandreoli, Federica; Ferrari, Davide; Guidetti, Veronica; Motta, Federico; Missier, Paolo
abstract

As Big Data Analysis meets healthcare applications, domain-specific challenges and opportunities materialize in all aspects of data science. Advanced statistical methods and Artificial Intelligence (AI) on Electronic Health Records (EHRs) are used both for knowledge discovery purposes and clinical decision support. Such techniques enable the emerging Predictive, Preventative, Personalized, and Participatory Medicine (P4M) paradigm. Working with the Infectious Disease Clinic of the University Hospital of Modena, Italy, we have developed a range of Data-Driven (DD) approaches to solve critical clinical applications using statistics, Machine Learning (ML) and Big Data Analytics on real-world EHRs. Here, we describe our perspective on the challenges we encountered. Some are connected to medical data and their sparse, scarce, and unbalanced nature. Others are bound to the application environment, as medical AI tools can affect people's health and life. For each of these problems, we report some available techniques to tackle them, present examples drawn from our experience, and propose which approaches, in our opinion, could lead to successful real-world, end-to-end implementations. DESY report number: DESY-22-153.

2022 - The interplay of post-acute COVID-19 syndrome and aging: a biological, clinical and public health approach [Articolo su rivista]
Guaraldi, Giovanni; Milic, Jovana; Cesari, Matteo; Leibovici, Leonard; Mandreoli, Federica; Missier, Paolo; Rozzini, Renzo; Cattelan, Anna Maria; Motta, Federico; Mussini, Cristina; Cossarizza, Andrea
abstract

The post-acute COVID-19 syndrome (PACS) is characterized by the persistence of fluctuating symptoms over three months from the onset of the possible or confirmed COVID-19 acute phase. Current data suggests that at least 10% of people with previously documented infection may develop PACS, and up to 50-80% of prevalence is reported among survivors after hospital discharge. This viewpoint will discuss various aspects of PACS, particularly in older adults, with a specific hypothesis to describe PACS as the expression of a modified aging trajectory induced by SARS CoV-2. This hypothesis will be argued from biological, clinical and public health view, addressing three main questions: (i) does SARS-CoV-2-induced alterations in aging trajectories play a role in PACS?; (ii) do people with PACS face immuno-metabolic derangements that lead to increased susceptibility to age-related diseases?; (iii) is it possible to restore the healthy aging trajectory followed by the individual before pre-COVID?. A particular focus will be given to the well-being of people with PACS that could be assessed by the intrinsic capacity model and support the definition of the healthy aging trajectory.

2022 - The pathway of NAFLD vs MAFLD toward significant fibrosis [Abstract in Atti di Convegno]
Milić, Jovana; Renzetti, Stefano; Motta, Federico; Gozzi, Licia; Besutti, Giulia; Burastero, Giulia; Iadisernia, Vittorio; Dessilani, Andrea; Del Monte, Martina; Faltoni, Matteo; Volpi, Sara; Lebouche, Bertrand; Al Hinai, Shaima; Deschenes, Marc; Calza, Stefano; Raggi, Paolo; Mancini, Giuseppe; Mussini, Cristina; Sebastiani, Giada; Guaraldi, Giovanni
abstract

2022 - The pathway of NAFLD vs MAFLD toward significant fibrosis [Abstract in Atti di Convegno]
Milić, Jovana; Renzetti, Stefano; Motta, Federico; Gozzi, Licia; Besutti, Giulia; Burastero, Giulia; Iadisernia, Vittorio; Lebouché, Bertrand; Al Hinai, Shaima; Deschenes, Marc; Calza, Stefano; Mussini, Cristina; Sebastiani, Giada; Guaraldi, Giovanni
abstract

2021 - An HMM-ensemble approach to predict severity progression of ICU treatment for hospitalized COVID-19 patients [Relazione in Atti di Convegno]
Mandreoli, Federica; Motta, Federico; Missier, Paolo
abstract

COVID-19-related pneumonia requires different modalities of Intensive Care Unit (ICU) interventions at different times to facilitate breathing, depending on severity progression. The ability for clinical staff to predict how patients admitted to hospital will require more or less ICU treatment on a daily basis is critical to ICU management. For real datasets that are sparse and incomplete and where the most important state transitions (dismissal, death) are rare, a standard Hidden Markov Model (HMM) approach is insufficient, as it is prone to overfitting. In this paper we propose a more sophisticated ensemble-based approach that involves training multiple HMMs, each specialized in a subset of the state transitions, and then selecting the more plausible predictions either by selecting or combining the models. We have validated the approach on a live dataset of about 1,000 patients from a partner hospital. Our results show that rare events, as well as the transitions to the most severe treatments outperform state of the art approaches.

2021 - An HMM–ensemble approach to predict severity progression of ICU treatment for hospitalized Covid–19 patients [Esposizione]
Mandreoli, Federica; Motta, Federico; Missier, Paolo
abstract

COVID–19–related pneumonia requires different modalities of Intensive Care Unit (ICU) interventions at different times to facilitate breathing, depending on severity progression. The ability for clinical staff to predict how patients admitted to hospital will require more or less ICU treatment on a daily basis is critical to ICU management. For real datasets that are sparse and incomplete and where the most important state transitions (dismissal, death) are rare, a standard Hidden Markov Model (HMM) approach is insufficient, as it is prone to overfitting. In this paper we propose a more sophisticated ensemble-based approach that involves training multiple HMMs, each specialized in a subset of the state transitions, and then selecting the more plausible predictions either by selecting or combining the models. We have validated the approach on a live dataset of about 1,000 patients from a partner hospital. Our results show that rare events, as well as the transitions to the most severe treatments outperform state of the art approaches.

2021 - Voice assistance to develop a participatory research and action to improve health trajectories of people with PACS [Abstract in Atti di Convegno]
Caselgrandi, Agnese; Milić, Jovana; Motta, Federico; Belli, Michela; Venuta, Maria; Aprile, Emanuele; Gozzi, Licia; Burastero, Giulia; Iadisernia, Vittorio; Yaacoub, Dina; Orsini, M.; Pacchioni, M.; Mescoli, E.; Mussini, Cristina; Guaraldi, Giovanni
abstract