Toksični učinki kokaina na srčnožilni sistem in obravnava bolnikov z bolečino v prsih zaradi zlorabe kokaina

V zadnjih 10 letih beležimo porast zlorabe kokaina v številnih državah zahodne in osrednje Evrope, tudi v Sloveniji. Zloraba kokaina je pomemben dejavnik tveganja za razvoj srčnožilnih bolezni, ki jih glede na čas izpostavljenosti kokainu razdelimo na akutne in kronične. Ocenjuje se, da približno 40 % uporabnikov kokaina vsaj enkrat doživi angino pektoris zaradi zlorabe kokaina, 6 % teh bolnikov pa razvije miokardni infarkt. V klinični praksi algoritem obravnave bolnikov z akutno bolečino v prsih zaradi zlorabe kokaina ni poenoten, zato se zdravljenje teh bolnikov pomembno razlikuje od kontrolne skupine bolnikov z akutno prsno bolečino, ki ni posledica zlorabe kokaina. Tako se pri vsakdanjem kliničnem delu zastavljajo številna vprašanja glede varnosti in koristi zdravljenja z zaviralci receptorjev beta pri bolnikih z akutno prsno bolečino zaradi zlorabe kokaina. Prepoznati zlorabo kokaina je pomembno pri tistih bolnikih s prsno bolečino, ki potrebujejo dvotirno protitrombotično zdravljenje po perkutanem koronarnem posegu z vstavitvijo žilne opornice, saj je pri uporabnikih kokaina zaradi nizke adherence povečano tveganje za trombozo žilne opornice. Prav tako ni popolnoma jasno, kateri dejavniki povečajo tveganje za možgansko krvavitev zaradi zlorabe kokaina, kar je potrebno upoštevati pred uvedbo protitrombotičnega zdravljenja. Vse to je sprožilo pobudo po prenovi smernic Ameriškega združenja za srce oz. izdelavi priporočil Evropskega združenja za kardiologijo, ki bodo standardizirale obravnavo bolnikov z akutno bolečino v prsih zaradi zlorabe kokaina, tudi v Sloveniji

Adverse drug reactions in the ambulatory internal patients at the emergency department: Focus on causality assessment and drug-drug interactions

A non-interventional retrospective study in ambulatory patients was conducted at the emergency department of the Division of internal medicine. In 2 months, 266 suspected adverse drug reactions (ADRs) were identified in 224/3453 patients (6.5 %). In 158/3453 patients (4.6 %), an ADR was the reason for emergency department visit and in 49 patients (1.4 %), ADRs led to hospitalisation. A causality assessment algorithm was developed, which included Naranjo algorithm and levels of ADR recognition by the treating physician and the investigators. Using this algorithm, 63/266 ADRs (23.7 %) were classified as “certain”, whereas using solely the Naranjo score calculation, only 19/266 ADRs (7.1 %) were assessed as “probable” or “certain”, and the rest of ADRs (namely, 247/266 = 92.9 %) were assessed as “possible”. There were 116/266 (43.6 %) ADRs related to potential drug-drug interactions (DDIs), stated in at least one of the literature sources used. Based on the causality relationship, the rate of the clinically expressed DDIs was 19.0 %, or 12/63 “certain” ADR cases. Of these, 10 cases presented serious DDI-related ADRs. In summary, ADR causality assessment based exclusively on Naranjo algorithm demonstrated low sensitivity at an ambulatory emergency setting. Additional clinical judgment, including the opinion of the treating physician, proved necessary to avoid under-rating of the causality relationship, and enabled the determination of clinically expressed DDIs

Application of machine learning for hematological diagnosis

Quick and accurate medical diagnosis is crucial for the successful treatment of a disease. Using machine learning algorithms, we have built two models to predict a hematologic disease, based on laboratory blood test results. In one predictive model, we used all available blood test parameters and in the other a reduced set, which is usually measured upon patient admittance. Both models produced good results, with a prediction accuracy of 0.88 and 0.86, when considering the list of five most probable diseases, and 0.59 and 0.57, when considering only the most probable disease. Models did not differ significantly from each other, which indicates that a reduced set of parameters contains a relevant fingerprint of a disease, expanding the utility of the model for general practitioner's use and indicating that there is more information in the blood test results than physicians recognize. In the clinical test we showed that the accuracy of our predictive models was on a par with the ability of hematology specialists. Our study is the first to show that a machine learning predictive model based on blood tests alone, can be successfully applied to predict hematologic diseases and could open up unprecedented possibilities in medical diagnosis.Comment: 15 pages, 6 figure

COVID-19 diagnosis by routine blood tests using machine learning

Physicians taking care of patients with coronavirus disease (COVID-19) have described different changes in routine blood parameters. However, these changes, hinder them from performing COVID-19 diagnosis. We constructed a machine learning predictive model for COVID-19 diagnosis. The model was based and cross-validated on the routine blood tests of 5,333 patients with various bacterial and viral infections, and 160 COVID-19-positive patients. We selected operational ROC point at a sensitivity of 81.9% and specificity of 97.9%. The cross-validated area under the curve (AUC) was 0.97. The five most useful routine blood parameters for COVID19 diagnosis according to the feature importance scoring of the XGBoost algorithm were MCHC, eosinophil count, albumin, INR, and prothrombin activity percentage. tSNE visualization showed that the blood parameters of the patients with severe COVID-19 course are more like the parameters of bacterial than viral infection. The reported diagnostic accuracy is at least comparable and probably complementary to RT-PCR and chest CT studies. Patients with fever, cough, myalgia, and other symptoms can now have initial routine blood tests assessed by our diagnostic tool. All patients with a positive COVID-19 prediction would then undergo standard RT-PCR studies to confirm the diagnosis. We believe that our results present a significant contribution to improvements in COVID-19 diagnosis.Comment: 11 pages, 4 figures, 2 table

Zaščitna vloga astrocitov pri zastrupitvi z ogljikovim monoksidom in optimizacija zdravljenja

Izhodišča: Zastrupitev z ogljikovim monoksidom (CO) močno okrne funkcijo astrocitov in nevronov. Pozne nevropsihološke posledice zastrupitve lahko preprečimo z zdravljenjem s hiperbaričnim kisikom (HBO). V raziskavi smo preučevali učinek CO in HBO na zgodnje procese celične smrti v nevronski in mešani kulturi ter ugotavljali, ali se raven glutationa v astrocitih po izpostavljenosti CO in HBO spremeni in ali bi lahko le-ta bil možna nova tarča za zdravljenje zastrupitve s CO. Metode: Primarne astrocitne, nevronske in mešane celične kulture možganske skorje podgane smo izpostavili 3.000 ppm CO v zraku, nato pa jih v obdobju 24-urne normoksije v različnih časovnih presledkih za 1 uro izpostavili 100-odstotnemu kisiku pri tlaku 3 barov. V celicah mešane in nevronske kulture smo merili aktivnost laktat dehidrogenaze (LDH) in kaspaz 3/7, v astrocitih pa raven glutationa. Rezultati: CO je povzročil zvišanje aktivnosti LDH in kaspaz 3/7 v nevronskih kulturah, v mešanih pa le zvišanje aktivnosti kaspaz 3/7. Po izpostavitvi CO in HBO se je zvišala aktivnost LDH v nevronskih kulturah in znižala aktivnost kaspaz 3/7 v mešanih kulturah. CO je v astrocitih povzročil znižanje celokupnega glutationa (GSHt), zvišanje glutation disulfida (GSSG) in znižanje GSH/GSSG, po izpostavitvi CO in HBO pa se je zvišala GSHt, znižala GSSG in zvišala GSH/GSSG. Zaključek: Razlike v citotoksičnem delovanju CO in zaščitni vlogi HBO v nevronski, mešani in astrocitni kulturi kažejo, da so nevroni, ki rastejo brez astrocitov, v primerjavi z mešano kulturo dovzetnejši za škodljive učinke CO ter nakazujejo, da astrociti ob oksidativnem stresu poskušajo ščititi nevrone, ki so pri sintezi glutationa odvisni od njih

Interstitial pneumonitis after acetylene welding: A case report

Acetylene is a colorless gas commonly used for welding. It acts mainly as a simple asphyxiant. In this paper, however, we present a patient who developed a severe interstitial pneumonitis after acetylene exposure during aluminum welding. A 44-year old man was welding with acetylene, argon and aluminum electrode sticks in a non-ventilated aluminum tank for 2 h. Four hours after welding dyspnea appeared and 22 h later he was admitted at the Emergency Department due to severe respiratory insufficiency with pO2 = 6.7 kPa. Chest X-ray showed diffuse interstitial infiltration. Pulmonary function and gas diffusion tests revealed a severe restriction (55% of predictive volume) and impaired diffusion capacity (47% of predicted capacity). Toxic interstitial pneumonitis was diagnosed and high-dose systemic corticosteroid methylprednisolone and inhalatory corticosteroid fluticasone therapy was started. Computed Tomography (CT) of the lungs showed a diffuse patchy ground-glass opacity with no signs of small airway disease associated with interstitial pneumonitis. Corticosteroid therapy was continued for the next 8 weeks gradually reducing the doses. The patient's follow-up did not show any deterioration of respiratory function. In conclusion, acetylene welding might result in severe toxic interstitial pneumonitis that improves after an early systemic and inhalatory corticosteroid therapy