Search Results (173)

Background/Object: Early allograft dysfunction (EAD) and 90-day mortality are major challenges after liver transplantation (LT). Combined predictive utility of the delta neutrophil index (DNI), lactate clearance, and C-reactive protein/albumin ratio (CAR) in the post-LT setting remains unexplored. Methods: We conducted a single-center retrospective cohort study of 548 adult elective LT recipients (January 2010–December 2023). Serial DNI, lactate clearance, CRP, albumin, bilirubin, and INR were measured during the first postoperative week. Multivariate logistic regression identified MELD score and five postoperative predictors of 90-day mortality: Results: DNI ≥ 2.5 at POD 7 (OR 8.42, 95% CI 3.56–19.92), lactate clearance < 15% at 6 h post-reperfusion (OR 4.65), CRP/albumin ratio ≥ 8.5 at POD 3 (OR 3.28), total bilirubin ≥ 10 mg/dL at POD 7 (OR 2.94), and INR ≥ 1.6 at POD 7 (OR 2.18). A five-variable postoperative DLC composite score (0–5 points) achieved an AUC of 0.876 (95% CI 0.831–0.921) for 90-day mortality, significantly outperforming DNI alone (AUC 0.742), MELD (AUC 0.663; both p < 0.001), and a bilirubin/INR subset of the Olthoff EAD criteria (AUC 0.784; p = 0.014). In a secondary concordance analysis, the DLC score showed concordance with patients meeting EAD criteria (AUC 0.854). Risk stratification produced 90-day mortality of 2.7%, 18.4%, and 55.3% for Low-, Intermediate-, and High-risk groups. Conclusions: The DLC score demonstrates promising internal performance for risk stratification within the first postoperative week and warrants prospective external validation prior to clinical adoption. Full article

Background: Inflammation plays a central role in the pathophysiology and clinical progression of acute myocardial infarction (MI). Although clopidogrel is known to exert potential anti-inflammatory effects in addition to platelet inhibition, the behavior of the early inflammatory response after treatment initiation and its prognostic significance remain unclear. This study aimed to evaluate dynamic inflammatory changes during the early post-treatment period and their association with in-hospital outcomes in patients with ST-elevation myocardial infarction. Methods: This retrospective, observational, single-center study included 300 patients with ST-elevation myocardial infarction treated with clopidogrel loading therapy. Systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), and delta neutrophil index (DNI) were evaluated before treatment and 24–48 h after therapy. In-hospital mortality and major adverse in-hospital outcomes were analyzed. Receiver operating characteristic analysis and multivariable logistic regression models were used to determine prognostic performance and independent predictors. Results: Contrary to the expected anti-inflammatory effect, all inflammatory markers significantly increased after clopidogrel therapy (all p < 0.05). Patients with persistent or increased inflammatory response demonstrated significantly higher rates of in-hospital mortality and major adverse outcomes. Post-treatment SIRI showed the strongest predictive performance for mortality (AUC: 0.81, 95% CI: 0.75–0.87), followed by ΔSIRI (AUC: 0.79). Multivariable analyses identified higher post-treatment inflammatory burden and dynamic inflammatory increases, particularly post-DNI and ΔSIRI, as independent predictors of mortality and adverse clinical course. Conclusions: Early inflammatory activation after clopidogrel therapy is strongly associated with poor in-hospital outcomes in patients with acute myocardial infarction. Persistent or increasing inflammatory burden may reflect an uncontrolled inflammatory response despite antiplatelet treatment and may serve as a practical and powerful prognostic marker for early risk stratification. Full article

Background: Dietitians play a critical role in preventing food–drug interactions (FDIs) and drug–nutrient interactions (DNIs); however, evidence regarding their knowledge, attitudes, and practices (KAP) in Saudi Arabia remains limited. Objective: The aim of this study was to assess dietitians’ KAP regarding FDIs and DNIs and examine their associations with socio-demographic and professional characteristics. Methods: A national cross-sectional study was conducted among 353 dietitians using a validated and modified questionnaire. Knowledge was assessed via 15 multiple-choice items (score range 0–15) and categorized as poor (0–5), moderate (6–10), or good (11–15). Attitudes were assessed using 8 Likert-scale statements (score range 8–40) and classified as negative (8–19), neutral (20–29), or positive (30–40). Practices were assessed via 6 frequency-scale items (score range 6–30) and categorized as poor (6–14), moderate (15–23), or good (24–30). Associations were analyzed using chi-square tests. Results: In total, 65.2% of participants demonstrated poor knowledge. Knowledge level was significantly associated with nationality (p = 0.011), educational qualification (p = 0.042), attendance at FDI/DNI training courses (p = 0.003), and inclusion of related topics during university education (p = 0.013). Higher knowledge levels were also associated with managing digestive diseases (p = 0.001), cardiovascular diseases (p = 0.020), and cancer (p = 0.031). Positive attitudes were reported by 77.6% of participants and were significantly associated with managing cardiovascular disease (p < 0.001) and obesity (p = 0.008). Good practices were observed in 36.3% of dietitians and were significantly associated with younger age (p = 0.024), more recent graduation (p = 0.006), fewer years of professional experience (p = 0.002), and managing obesity (p = 0.016). Knowledge was positively associated with practice (p < 0.001). Conclusions: Despite generally positive attitudes, substantial gaps in knowledge and practice regarding FDIs and DNIs exist among dietitians in Saudi Arabia. Strengthening academic curricula and continuing professional education is essential to enhance competency and improve patient safety. Full article

Digital new infrastructure (DNI) has emerged as a pivotal force in reshaping regional economic geography. However, the existing literature primarily focuses on its macro-growth effects, leaving a significant gap regarding whether it effectively bridges the divide in Digital–Real Economy Integration (DRI) and promotes sustainable regional development. Based on panel data from 31 provinces in China (2013–2023), this study employs a two-way fixed-effects model to empirically investigate the impact of DNI on the balanced development of DRI and its underlying mechanisms. The research findings are threefold: First, DNI acts as a critical driver of regional convergence in DRI by accelerating digital technology diffusion and facilitating digital talent mobility, thereby significantly mitigating inter-provincial disparities. Second, heterogeneity analysis reveals that the convergence effect of DNI is contingent upon regional environments: the “picking the winners” bias in fiscal expenditure inadvertently weakens the inclusive nature of DNI; conversely, a robust high-tech industrial base, financial deepening, and the “East Data, West Computing” strategy significantly enhance the convergence effect by strengthening technology absorption capacity and alleviating financing constraints. Third, threshold effect analysis confirms the catalytic role of marketization, demonstrating that a mature market environment effectively breaks the geographical stickiness of factor flows, thus amplifying the balanced growth dividends of DNI. This study not only extends the theory of regional convergence into the digital era but also provides critical policy insights for achieving sustainable and inclusive digital transformation. Full article

Atmospheric aerosols strongly regulate surface solar irradiance in tropical coastal environments through scattering and absorption. This study examines aerosol–irradiance interactions over Penang, Malaysia, using Aerosol Robotic Network (AERONET) observations of aerosol optical depth (AOD), single scattering albedo (SSA), and extinction Ångström exponent (AE); NASA’s Prediction of Worldwide Energy Resource (POWER) irradiance data; and Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) reanalysis for aerosol compositional context. Bottom-of-atmosphere radiative forcing efficiency (BOA RFE) was quantified for global, direct and diffuse irradiance (GHI, DNI and DHI) under clear- and all-sky conditions during 2014–2018. Results show persistent aerosol-induced attenuation of surface radiation, with GHI and DNI RFE predominantly negative, while DHI RFE remains consistently positive, indicating redistribution of solar energy from direct to diffuse components. Time resolved analysis reveals daily GHI RFE typically ranging from approximately −0.5 to −3.5 W m⁻² per unit AOD, with episodic excursions below −4 W m⁻² per AOD during high-aerosol events, whereas DNI RFE frequently reaches values below −0.8 W m⁻² per AOD, confirming its greater sensitivity to aerosol extinction. In contrast, DHI RFE commonly exceeds +5 W m⁻² per AOD and intermittently surpasses +10 W m⁻² per AOD, reflecting enhanced scattering and multiple-scattering effects. AOD-stratified analysis demonstrates a nonlinear weakening of forcing efficiency with increasing aerosol burden, with mean GHI RFE decreasing from approximately −1.6 to −0.4 W m⁻² per AOD between low- and high-AOD regimes, accompanied by corresponding reductions in DNI (−0.35 to −0.1 W m⁻² per AOD) and DHI (+3.3 to +0.8 W m⁻² per AOD). Overall, aerosol loading is identified as the dominant control on BOA radiative forcing efficiency in this tropical coastal environment, while SSA and AE act as secondary modulators. Full article

Achieving a balance between accuracy and computational efficiency in solar energy flux estimation models remains a key challenge in atmospheric radiative transfer research. Given the high computational cost of spectral models, a widely used simplification strategy consists of parameterizing atmospheric spectral transmittances through wavelength-averaging formulations. This study introduces a Clear-Sky Multivariable (CSMV) broadband parametric model derived from the Leckner spectral model for estimating the three components of solar irradiance under clear-sky conditions: direct normal irradiance (DNI), diffuse irradiance (Gd), and global irradiance (G). The model development follows a two-stage procedure. First, discrete broadband transmittances are obtained by applying an independent spectral integration scheme to the transmittances of the source spectral model. In the second stage, these discrete values are fitted with analytical functions expressed solely in terms of atmospheric state parameters, yielding wavelength-independent broadband formulations. While the overall development framework follows a classical parameterization approach, the calculation of the diffuse component introduces a novel way of estimating the fraction of aerosol scattering directed toward the ground. The model was tested against data collected from eight radiometric stations distributed across six continents and benchmarked against two well-established reference models. Overall, the results indicate a high level of accuracy and demonstrate the practical applicability of the model. Full article

This study proposes an advanced short-term Direct Normal Irradiance (DNI) prediction model for Concentrated Solar Power (CSP) systems, integrating a convolutional neural network (CNN) with a fitted clear-sky DNI model. Leveraging all-sky images and historical DNI data, the model precisely identifies cloud motion patterns through dense optical flow analysis and forecasts DNI using a targeted region-of-interest (ROI) approach. When maximum cloud pixel velocity falls below 5 pixels per minute, the clear-sky DNI model or persistence model directly applies; for higher-velocity conditions, the CNN predicts the clear-sky index to dynamically adjust the forecast. Experimental validation across diverse weather conditions demonstrates superior accuracy, achieving significantly lower normalized Mean Absolute Errors (nMAEs) and normalized Root Mean Squared Errors (nRMSEs) for various forecast horizons under cloudy skies compared to recent state-of-the-art deep learning approaches. This work delivers a robust solution for preventing thermal shock in the receiver and improving the CSP operational stability. Full article

Aim: This study aimed to evaluate the diagnostic performance of the Delta Neutrophil Index (DNI) and Neutrophil-to-Lymphocyte Ratio (NLR) in distinguishing malignant from benign primary brain tumors during the preoperative period. Methods: This retrospective cohort study was conducted at a tertiary university hospital. A total of 140 participants were included 60 patients with malignant glial tumors, 50 patients with benign brain tumors, and 30 healthy controls without inflammatory, infectious, or hematologic disease. Preoperative complete blood count results obtained within seven days before surgery were analyzed. Results: Patients with malignant tumors were significantly older than those in the benign and control groups (p < 0.001). DNI, NLR, PLR, MLR, and SII values were all significantly elevated in the malignant group (p < 0.001, for all comparisons). ROC analysis revealed high diagnostic accuracy for DNI (AUC = 0.847) and NLR (AUC = 0.850), with optimal cut-off values of 3.50 and 3.95, respectively. In multivariable logistic regression adjusted for age, DNI > 3.5 (OR = 20.67; 95% CI: 3.35–127.64; p = 0.001), NLR > 3.95 (OR = 21.17; 95% CI: 3.28–136.50; p = 0.001), and CRP (OR = 1.52; 95% CI: 1.20–1.93; p = 0.001) remained independent predictors of malignancy. The combined model including DNI and NLR achieved the highest diagnostic accuracy (AUC = 0.937; age-adjusted AUC = 0.943), with a sensitivity of 88.3% and a specificity of 86.0% after age adjustment. Conclusions: Both DNI and NLR demonstrated significant value in differentiating malignant from benign primary brain tumors prior to surgery, with DNI emerging as the most powerful independent predictor. The combined use of DNI and NLR substantially improved diagnostic accuracy, suggesting that simple hematologic indices may serve as practical, noninvasive adjunctive tools in the preoperative assessment of brain tumor malignancy. These markers may assist in surgical prioritization, patient counseling, and clinical decision-making, particularly in resource-limited settings. Full article

Background/Objectives: Septic arthritis of native joints is an orthopedic emergency in which rapid discrimination from non-infectious arthritis is crucial. Because cartilage damage can occur within hours, urgent irrigation and debridement are often pursued on an emergency basis (ideally within the first 6–8 h) of presentation, underscoring the need for rapidly available biomarkers. The delta neutrophil index (DNI) quantifies circulating immature granulocytes and may complement conventional inflammatory biomarkers such as C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), white blood cell count (WBC), and procalcitonin (PCT). We evaluated the diagnostic performance of DNI for native-joint septic arthritis against both microbiologic and clinical reference standards. Methods: We retrospectively analyzed 85 adults who underwent surgical irrigation and debridement for suspected native joint septic arthritis at a tertiary center. Serum CRP, ESR, WBC, DNI, and PCT (available in 67 patients) were recorded together with synovial leukocyte counts. Infection status was defined using either positive synovial culture (microbiologic reference) or clinical adjudication according to the Guideline for management of septic arthritis in native joints (SANJO). Diagnostic performance was assessed using receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC); exploratory cut-offs were identified by the Youden index, and pairwise AUCs were compared using DeLong’s test. Results: Synovial leukocyte analysis was highly sensitive but poorly specific (sensitivity 92.9%, specificity 10.3%). Against culture, DNI showed the highest discrimination (AUC = 0.914), exceeding CRP (0.687), ESR (0.643), WBC (0.648), and PCT (0.697); DeLong ΔAUC vs. CRP 0.227 (p < 0.001), ESR 0.270 (p < 0.001), WBC 0.266 (p < 0.001), PCT 0.227 (p = 0.001). At pre-specified cut-offs, DNI showed the most balanced sensitivity/specificity (94.3%/84.0%), corresponding to a negative predictive value (NPV) of 95.5% (42/44) and a positive predictive value (PPV) of 80.5% (33/41) against culture in this cohort. Against clinical infection, DNI outperformed others (AUC:0.921; ΔAUC vs. CRP = 0.204, ESR = 0.343, WBC = 0.244, PCT = 0.295; all p < 0.001). As a rule-in threshold, DNI ≥ 0.6 yielded a specificity of 100% with a sensitivity of 73.2%. In culture-negative patients (infected n = 21, uninfected n = 29), DNI remained discriminatory (AUC 0.80, p < 0.001), whereas other biomarkers were not. Conclusions: DNI demonstrated superior diagnostic accuracy compared with conventional inflammatory biomarkers. As a rapid parameter available with the initial complete blood count, DNI may support early risk stratification and rule-in decisions within the first hours of presentation; however, it should be used as a supplementary indicator alongside synovial fluid analysis and clinical assessment rather than as a stand-alone diagnostic tool. Full article

The accurate short-term forecasting of global horizontal irradiance (GHI) is essential to optimizing the operation and integration of solar energy systems into the power grid. This study evaluates the performance of the Weather Research and Forecasting (WRF) model in predicting GHI over a 48 h forecast horizon at an Italian site: the ENEA Casaccia Research Center, near Rome (central Italy). The instantaneous GHI provided by WRF at model output frequency was post-processed to derive the mean GHI over the preceding hour, consistent with typical energy forecasting requirements. Furthermore, a decomposition model was applied to estimate direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) from the forecasted GHI. These derived components enable the estimation of solar energy yield for both concentrating solar power (CSP) and photovoltaic (PV) technologies (on tilted surfaces) by accounting for direct, diffuse, and reflected components of solar radiation. Model performance was evaluated against ground-based pyranometer and pyrheliometer measurements by using standard statistical indicators, including RMSE, MBE, and correlation coefficient (r). Results demonstrate that WRF-based forecasts, combined with suitable post-processing and decomposition techniques, can provide reliable 48 h predictions of GHI and DNI at the study site, highlighting the potential of the WRF framework for operational solar energy forecasting in the Mediterranean region. Full article

Dilated cardiomyopathy is characterized by progressive left ventricular dilation and impaired systolic function, with inflammation recognized as a key contributor to disease onset and adverse outcomes. C-reactive protein reflects systemic biochemical inflammation, whereas Delta Neutrophil Index represents the circulating immature neutrophil fraction and provides a cellular dimension of inflammatory burden. The combined diagnostic and prognostic value of these two biomarkers in dilated cardiomyopathy has not been adequately explored. This retrospective study included one hundred and fifty patients with dilated cardiomyopathy and one hundred and fifty age-, diabetes-, and hypertension-matched controls. Demographic, laboratory, and echocardiographic measurements were analyzed. The diagnostic and prognostic performances of C-reactive protein, Delta Neutrophil Index, and their combined model were assessed using logistic regression, receiver operating characteristic curve analysis, reclassification metrics, calibration testing, and decision curve analysis. Additional analyses were performed for patients with left ventricular ejection fraction below twenty percent, and mortality predictors were examined within the dilated cardiomyopathy cohort. Both C-reactive protein and Delta Neutrophil Index levels were significantly higher in patients with dilated cardiomyopathy than in controls and were further elevated in those with severely reduced ejection fraction. Delta Neutrophil Index remained independently associated with severe left ventricular dysfunction (ejection fraction ≤ 20%) in multivariable analysis (odds ratio 2.51). Each biomarker showed an independent association with the presence of dilated cardiomyopathy, and their combined model achieved the highest diagnostic accuracy. In receiver operating characteristic analysis, the area under the curve was 0.895 for Delta Neutrophil Index, 0.691 for C-reactive protein, and increased to 0.920 for the combined model, with a sensitivity of 81.3% and specificity of 92.0%. Delta Neutrophil Index was independently associated with severe left ventricular dysfunction and mortality, while C-reactive protein, age, ejection fraction, urea, and sodium also contributed to mortality risk. Delta Neutrophil Index was independently associated with mortality (odds ratio 2.51), while C-reactive protein, age, ejection fraction, urea, and sodium also contributed to mortality risk. The combined model provided significant improvement in risk reclassification and demonstrated superior calibration and greater net clinical benefit across a wide range of decision thresholds. C-reactive protein and Delta Neutrophil Index offer complementary diagnostic and prognostic information in dilated cardiomyopathy. Their combined use enhances diagnostic discrimination, strengthens risk stratification, and improves identification of patients at high risk for severe ventricular dysfunction and mortality. Incorporation of these accessible biomarkers into clinical evaluation may support earlier recognition and more tailored management of high-risk individuals. Full article

Background: Use of high-flow nasal cannula (HFNC) expanded from ICUs to internal medicine/respiratory wards during and after the COVID-19 pandemic, but safety and effectiveness in non-ICU settings remain uncertain. Methods: We performed a systematic review and meta-analysis of adults (≥18 years) initiated on HFNC in non-ICU wards. Primary outcomes were in-hospital (or 28-day) mortality and ICU transfer; where available, we compared mortality for HFNC vs. conventional oxygen therapy (COT) in do-not-intubate (DNI) cohorts. Observational studies and trials were eligible. Random-effects models synthesized proportions and risk ratios; risk of bias (ROBINS-I/RoB 2) and certainty (GRADE) were assessed. Results: Ten studies met the inclusion criteria for any-ward HFNC; subsets contributed data to pooled analyses. Across all non-ICU wards (general wards plus step-up IMCU/HDU), pooled mortality was 14.0% (95% CI 4.6–35.5; I² ≈ 92%). Pooled ICU transfer after ward/step-up HFNC start was 20.0% (95% CI 6.3–48.1; I² ≈ 97%). Restricted to internal medicine/respiratory wards, pooled mortality was 19.8% (95% CI 7.1–44.2; I² ≈ 95%) and ICU transfer 31.2% (95% CI 9.9–65.0; I² ≈ 97%). In step-up units (IMCU/HDU), ICU transfer appeared lower and less variable (22.0% [95% CI 16.5–28.8]; I² ≈ 10%), suggesting environment-dependent outcomes. In a multicenter DNI COVID-19 cohort, HFNC vs. COT showed no clear mortality difference (RR ≈ 0.90, 95% CI 0.75–1.08; adjusted OR ≈ 0.72, 95% CI 0.34–1.54). Certainty of evidence for all critical outcomes was very low due to observational design, high inconsistency, and imprecision. Conclusions: HFNC outside the ICU is feasible, but it is related to nontrivial mortality and frequent escalation—particularly on general wards—while step-up units demonstrate more reproducible trajectories. Outcomes appear strongly conditioned by care environment, staffing, monitoring, and escalation pathways. Given very low certainty and substantial heterogeneity, institutions should pair ward HFNC with protocolized reassessment and rapid response/ICU outreach, and future research should prospectively compare ward HFNC pathways against optimized COT/NIV using standardized outcomes. Full article

Background/Objectives: The delta neutrophil index (DNI)—a hematology analyzer-derived measure of circulating immature granulocytes—may assist pre-biopsy decision-making, yet its behavior across tumor types is incompletely defined. We examined whether pre-biopsy DNI differs by pathology category, tumor class, and definitive histology, and evaluated diagnostic performance. Methods: In this retrospective, single-center cohort, consecutive inpatients with malignancy were screened (n = 2009). Exclusions included positive blood cultures, prior chemotherapy/radiotherapy before index labs, and lack of definitive pathology, yielding 1313 analyzable cases. All laboratories, including DNI, were obtained before diagnostic biopsy. DNI was assessed as a continuous variable and categorized (Zero = 0; High > 0.6). Groupwise differences used Kruskal–Wallis and χ² tests with FDR control; discrimination used ROC analyses (one-versus-rest/pairwise). Results: DNI distributions differed across pathology, tumor class, and definitive diagnoses (all p < 0.001). High DNI (>0.6) and Zero DNI (=0) proportions also varied significantly by grouping. Hematologic malignancies showed the highest DNI (median ~1.0) compared with sarcoma and carcinoma (medians ~0.4). Using DNI alone, one-versus-rest AUCs were 0.735 (hematologic), 0.692 (melanoma), 0.672 (sarcoma), and 0.652 (carcinoma); the strongest pairwise separation was hematologic versus sarcoma (AUC 0.780). For specific solid tumors, including breast and renal cell carcinoma, single-marker discrimination was modest; no clinically actionable RCC cutoff emerged. Sensitivity analyses restricted to culture-negative cases yielded consistent findings. Conclusions: Pre-biopsy DNI exhibits tumor-type-dependent variation and provides adjunct diagnostic signal—the strongest for hematologic malignancy—yet is insufficient alone for solid tumor subtyping. Integration with clinical assessment and routine biomarkers, and multi-center validation with device harmonization are warranted. Full article

Efficient utilization of solar energy in equatorial regions depends on accurately determining the optimal tilt and azimuth angles of fixed photovoltaic (PV) systems. This study presents a three-dimensional vector-based methodology that employs Direct Normal Irradiance (DNI) to estimate the mean direction of incident solar flux. Hourly DNI data from 2020–2024 for the city of Guayaquil, Ecuador, were transformed into spatial vectors and integrated to obtain a resultant vector representing the average orientation and elevation of direct solar radiation. The analysis yielded an optimal tilt angle of 5.73° and an azimuth of 59.15°, values consistent with Guayaquil’s equatorial latitude and previous studies conducted in tropical environments. The low tilt angle reflects the persistently high solar elevation typical of equatorial zones, while the slight northeastward orientation deviation corresponds to the asymmetric diurnal distribution of solar irradiance. The main contribution of this work lies in providing a geometrically rigorous and computationally efficient approach capable of synthesizing the directional behavior of solar flux without relying on complex transposition models. The proposed method enhances the optimization of PV system design, urban energy planning, and renewable microgrid modeling in data-scarce contexts, supporting the sustainable development of solar energy in equatorial regions. Full article

Background: The role of age and time-dependent variables in determining the response to disease-modifying therapies (DMTs) has aroused growing interest in the Multiple Sclerosis (MS) field. Although it is a very hot topic, related literature on the subject is considerably lacking. Objectives: The aim of this study is to deepen the understanding of how time-dependent variables influence disability accumulation and drug response in an MS population, assuming DMF as the first-line treatment, and to expand our knowledge of the risk–benefit evaluation of DMF. Methods: We investigated, in a real-world setting, the efficacy of Dimethyl Fumarate (DMF) in naive versus switcher MS patients, correlated with age, in preventing disability accumulation. Starting from an initial population of 234 DMF-treated patients, we selected 169 of them based on their similar time in therapy (TinT) with DMF of 5.9 ± 2.3 year and sex ratio. Of these, 74 were naive and 95 were lateral switchers at the start of treatment. The mean Expanded Disability Status Scale (EDSS), Disease Duration (DD), age and age at onset were compared between groups. Results: The switcher group showed higher EDSS and age compared to the naive group (2.7 vs. 1.8, p < 0.001; 40.2 vs. 35.5, p = 0.005, respectively). Age correlated with DD, EDSS and age at onset in both naive (r = 0.39, p = 0.007; r = 0.53, p = 0.000; r = 0.63, p = 0.000, respectively) and switcher (r = 0.46, p = 0.002; r = 0.49, p = 0.000; r = 0.61, p = 0.000, respectively) groups. Kaplan–Meier curves, adjusted for age, also indicated that the naive group retained an EDSS score status of 0.5–3.5 more frequently (p < 0.001) and reached elevated disability less frequently (p = 0.002) than switchers. The mean EDSS percentage ratio between paired naive and switcher patients, representing the differential neurological impairment (DNI), was 69%, inversely correlating with age in both naive (r = −0.52, p < 0.001) and switcher patients (r = −0.47, p < 0.001). Finally, logistic regression analysis indicated age as an independent and predictive variable with respect to EDSS. Conclusions: We conclude that age is the main contributor to disability progression and the primary predictive factor for treatment effectiveness for DMF in both naive and switcher MS patients. Full article