Prehospital Prediction of Hypokalemia in patients with ST‑Segment Elevation Myocardial Infarction: Development and Validation of a Prediction Model

Prehospital Prediction of Hypokalemia in patients with ST‑Segment Elevation Myocardial Infarction: Development and Validation of a Prediction Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prehospital Prediction of Hypokalemia in patients with ST‑Segment Elevation Myocardial Infarction: Development and Validation of a Prediction Model Qian Gu, Chao Tang, Bao jun Yang, Fei Shi, Xiao song Gu, Jing Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8185679/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Hypokalemia is common in patients with ST-segment elevation myocardial infarction (STEMI) and significantly elevates the risk of life-threatening arrhythmias and mortality. Yet no validated prehospital prediction tool exists to identify this high-risk condition early. Objective To develop and validate a prehospital prediction model for hypokalemia in STEMI patients using readily available clinical and electrocardiographic parameters. Methods A retrospective observational study was conducted involving 320 STEMI patients admitted to the Second Affiliated Hospital of Soochow University between January 2023 and December 2024. Patients were categorized into hypokalemia (n = 114) and non-hypokalemia (n = 206) groups based on initial serum potassium levels. Univariate logistic regression, least absolute shrinkage and selection operator(LASSO), and multivariate logistic regression were used to identify independent predictors. A nomogram was constructed and evaluated for discrimination, calibration, and clinical utility. Results Five independent predictors were identified: symptom-to-door time (OR = 0.85, 95% CI: 0.78–0.94), syncope/coma (OR = 3.57, 95% CI: 1.12–11.37), atrial arrhythmia (OR = 4.18, 95% CI: 1.33–13.17), PR interval (OR = 1.01, 95% CI: 1.00–1.02), and U wave (OR = 5.20, 95% CI: 2.59–10.46). The prediction model demonstrated good discrimination with an AUC of 0.735 (95% CI: 0.680–0.791). Calibration curves and decision curve analysis confirmed satisfactory model performance and clinical usefulness. Conclusion We developed a practical and validated nomogram for predicting prehospital hypokalemia in STEMI patients using five easily obtainable clinical and ECG variables. This tool may facilitate early identification and intervention in high-risk individuals, potentially improving prehospital management and clinical outcomes. ST-segment elevation myocardial infarction pre-hospital assessment prediction model nomogram hypokalemia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Heart function fundamentally depends on the precise generation and propagation of action potentials, in which potassium channels play a crucial role. Hypokalemia is defined as a serum potassium level below 3.5 mmol/L, which severely disrupts the stability of cardiac electrophysiology and leads to the occurrence of arrhythmia, especially during acute myocardial ischemia[ 1 ]. After myocardial infarction, fibroblasts establish electrical coupling with surviving myocardial cells, causing abnormal changes in potassium channels and inducing arrhythmia[ 2 ]. Multiple clinical studies have also confirmed that abnormal potassium ion concentration significantly increases the risk of malignant arrhythmia and affects patient prognosis[ 3 , 4 ]. The risk of arrhythmia can be significantly heightened in MI patients with hypokalemia even before revascularization is performed[ 5 ]. Hypokalemia patient may present with symptoms such as weakness, nausea, vomiting, coma, or syncope—manifestations that can overlap with those of myocardial infarction. In clinical practice, it is not accurate to identify concurrent hypokalemia in ST-segment elevation myocardial infarction (STEMI) patients based solely on single symptoms or physical signs. The diagnosis of hypokalemia often relies on the detection of venous potassium levels. The unique susceptibility of cardiomyocytes to extensive damage from even brief ischemia means that patients may already be in the catheterization lab for revascularization before their hypokalemia is even identified. The electrophysiological instability induced by PCI and ischemia-reperfusion injury, aggravated by hypokalemia, results in a markedly increased susceptibility to and higher incidence of arrhythmias[ 6 ]. Although there have been studies exploring the relationship between blood potassium levels and STEMI prognosis, most research has focused on in-hospital blood potassium monitoring, and there is relatively little research on predicting and intervening in pre-hospital blood potassium levels. If the high-risk population for hypokalemia can be quickly identified before or in the early stages of admission, and targeted interventions (such as preventive potassium supplementation) can be implemented, it may effectively reduce the incidence of malignant arrhythmias and improve patient prognosis. However, there is currently a lack of multi-indicator combination hypokalemia prediction models suitable for pre-hospital environments. Therefore, this study aims to retrospectively analyze the clinical data of STEMI patients, explore independent risk factors for prehospital hypokalemia, construct and validate a hypokalemia prediction model suitable for prehospital emergency scenarios, provide scientific basis for early identification of high-risk patients, optimize prehospital management strategies, and ultimately achieve the advancement and integration of the STEMI treatment chain. Materials and methods A retrospective observational study was conducted to collect cases of STEMI patients who were admitted to the emergency room of the Second Affiliated Hospital of Soochow University from January 2023 to December 2024. Inclusion criteria comprised: ①The diagnosis of patients with STEMI meets the diagnostic criteria of the 4th edition of the Global definition of myocardial infarction (2018)[ 7 ]; ②Age ≥ 18 years, electrocardiogram-confirmed STEMI, and primary percutaneous coronary intervention with stent deployment within 12 hours of symptom onset;③ Intravenous blood sampling for serum potassium completed within 1 hour of emergency department arrival;④Capable of communication, without psychiatric disorder, and able to provide accurate and valid clinical information. Exclusion criteria were as follows: ① Patients with incomplete clinical data (e.g., missing ECG or serum potassium records);② Presence of atrial fibrillation, atrial flutter, or any atrioventricular block on the emergency ECG, precluding reliable interpretation of PR intervals and P waves༛③ Administration of potassium supplementation or medications significantly altering potassium homeostasis before hospital admission༛④ Receipt of life-sustaining measures (e.g., endotracheal intubation, extracorporeal membrane oxygenation) prior to blood draw, or patients who died prior to hospital arrival༛⑤End-stage renal disease requiring maintenance hemodialysis༛⑥Active chemotherapy or radiotherapy for malignancy༛ Review and collect clinical data of patients with acute STEMI, including past disease history, family history, medication history, smoking history, and alcohol consumption history. The basic data of patients at the time of emergency reception, including gender, age, complications, risk factors (such as smoking, drinking, diabetes, hypertension, family history of cardiovascular and cerebrovascular diseases), cardiac and non-cardiac symptoms (such as chest pain, dyspnea, diaphoresis, vomiting, fatigue, etc.), onset to hospital time, pre hospital vital signs (blood pressure, heart rate), and emergency ECG parameters (heart rate, P-wave duration, PR interval, QRS duration, QT/QTc, U-wave, arrhythmia, infarction site, etc.). Arrhythmias include ventricular arrhythmias (such as premature ventricular contractions, transient ventricular tachycardia, etc.) and atrial arrhythmias (such as premature atrial contractions, atrial tachycardia etc.). All patients completed venous potassium collection within 1 hour of arrival at the emergency room, and concentrations were measured by direct ion-selective electrode with a reference interval of 3.5–5.1 mmol/l. This study divided patients into two groups based on their blood potassium levels. One group was the hypokalemia group (Blood potassium level is less than 3.5 mmol/L), with a total of 114 cases, including 101 males and 13 females; the other group was the non-hypokalemia group (Blood potassium level is above than 3.5 mmol/L), with a total of 206 cases, including 172 males and 34 females. Ethics approval and consent to participate This study was approved by the Ethics Committee of the Second Affiliated Hospital of Soochow University (Approval No. JD-HG-2025071). Due to the retrospective nature of the study, the need for informed consent was waived by the Ethics Committee of the Second Affiliated Hospital of Soochow University. The study was performed in accordance with the ethical standards as laid down in the Declaration of Helsinki. Statistical analysis Statistical analyses were performed using SPSS version 27.0 and R software (version 4.2.2). Normality was assessed using the Shapiro-Wilk test. Normally distributed variables are presented as mean ± standard deviation, Independent sample t-test is used for inter group comparison. Non-normally distributed variables were expressed as median (interquartile range), with between-group comparisons performed using the Wilcoxon rank-sum test. Categorical variables are presented as counts and percentages (n/%), with between-group comparisons conducted using χ² test or Fisher's exact test as appropriate. Screening independent influencing factors of hypokalemia through univariate logistic regression, least absolute shrinkage and selection operator（LASSO）, and multivariate logistic regression. Construct a diagnostic prediction model for prehospital hypokalemia in STEMI patients based on the independent influencing factors obtained and visualize it in a nomogram. Evaluate the predictive model from three aspects: discrimination, calibration, and clinical effectiveness. The discriminability is evaluated by plotting the ROC curve and calculating the area under the curve (AUC), the model calibration is validated by the calibration curve, and the clinical effectiveness of the model is evaluated using the clinical decision curve (DCA). Using Bootstrap method for internal validation of the model to obtain corrected AUC. Results Baseline characteristics Baseline characteristics revealed no significant differences in age, gender, BMI, or medical history between hypokalemic and non-hypokalemic patients. The distribution of infarct-related coronary territories did not differ significantly between the hypokalemic and non-hypokalemic cohorts. Clinically, hypokalemic patients presented with shorter symptom-to-door time (median 2.0 [IQR 1.0-3.0] vs. 2.0 [IQR 1.0-5.0] hours, p<0.001), lower admission systolic blood pressure (134±30 vs. 142±28 mmHg, p=0.025), and higher rates of diaphoresis and syncope/coma. Electrocardiographic analysis revealed significantly longer PR intervals in hypokalemic patients (175 [IQR 155-188] vs. 165 [IQR 151-180] ms, p=0.006), with higher prevalence of atrial arrhythmias and U-wave presence.（ Table 1 ） Table 1 . Comparisons of characteristics between hypokalemia and non-hypokalemia Variables Overall Hypokalemia group Non-hypokalemia group Statistic p Value N = 320 N = 206 N = 114 potassium 3.69 (3.40, 3.98) 3.32 (3.16, 3.41) 3.90 (3.72, 4.13) 23,484. <0.001 Basic information Age(years) 58 (48, 68) 58 (44, 69) 58 (52, 65) 11,627 0.885 BMI , kg/m² 25.1 (22.5, 27.1) 24.9 (22.2, 27.4) 25.1 (22.8, 27.0) 10,577 0.878 Male, n(%) 273 (85.3%) 172 (83.5%) 101 (88.6%) 1.52 0.217 Smoking 183 (57.2%) 117 (56.8%) 66 (57.9%) 0.04 0.849 Medical and medication history Hypertension 201 (62.8%) 132 (64.1%) 69 (60.5%) 0.4 0.529 Diabetes mellitus 70 (21.9%) 49 (23.8%) 21 (18.4%) 1.24 0.266 Prior MI 21 (6.6%) 16 (7.8%) 5 (4.4%) 1.37 0.242 Cerebrovascular disease 21 (6.6%) 15 (7.3%) 6 (5.3%) 0.49 0.485 ACEI/ARB 60 (18.8%) 42 (20.4%) 18 (15.8%) 1.02 0.313 CCB 78 (24.4%) 56 (27.2%) 22 (19.3%) 2.48 0.116 Beta-blocker 33 (10.3%) 21 (10.2%) 12 (10.5%) 0.01 0.925 Insulin 14 (4.4%) 8 (3.9%) 6 (5.3%) 0.33 0.577 Metformin 27 (8.4%) 18 (8.7%) 9 (7.9%) 0.07 0.795 Diuretics 15 (4.7%) 9 (4.4%) 6 (5.3%) 0.13 0.717 Disease situation Symptom-to-door time, h 2.0 (1.0, 4.0) 2.0 (1.0, 5.0) 2.0 (1.0, 3.0) 14,582. <0.001 SBP, mmHg 139 ± 29 142 ± 28 134 ± 30 2.26 0.025 DBP, mmHg 87 (72, 101) 88 (74, 101) 85 (71, 100) 12,647 0.253 Vomiting 67 (20.9%) 47 (22.8%) 20 (17.5%) 1.23 0.267 Diaphoresis 114 (35.6%) 82 (39.8%) 32 (28.1%) 4.41 0.036 Syncope/Coma 15 (4.7%) 6 (2.9%) 9 (7.9%) 4.08 0.043 Electrocardiogram parameters Heart rate, bpm 75 (65, 87) 75 (65, 85) 74 (62, 91) 11,705. 0.964 P-wave duration, ms 93 (87, 102) 93 (86, 100) 95 (89, 105) 10,157. 0.046 PR-interval, ms 169 (152, 183) 165 (151, 180) 175 (155, 188) 9,582.5 0.006 QRS duration, ms 98 (90, 104) 97 (91, 103) 98 (90, 104) 11,465. 0.727 QT interval, ms 367 (344, 395) 367 (345, 393) 368 (343, 401) 11,584. 0.842 QTc interval, ms 409 (392, 430) 408 (390, 430) 410 (395, 431) 11,108. 0.424 Atrial arrhythmia 16 (5.0%) 5 (2.4%) 11 (9.6%) 8.06 0.005 Ventricular arrhythmia 15 (4.7%) 13 (6.3%) 2 (1.8%) 3.41 0.065 U wave present 47 (14.7%) 16 (7.8%) 31 (27.2%) 22.1 <0.001 Myocardial infarction site 0.536 Extensive anterior wall 63 (19.7%) 37 (18.0%) 26 (22.8%) Anterior wall 122 (38.1%) 78 (37.9%) 44 (38.6%) Lateral wall 121 (37.8%) 80 (38.8%) 41 (36.0%) Posterior wall 14 (4.4%) 11 (5.3%) 3 (2.6%) Continuous data are presented as mean ± SD or median (Q1, Q3); categorical data are presented as count (%).P values are derived from t tests for continuous variables and chi-square tests for categorical variables. BMI body mass index, ACEI/ARB angiotensin-converting enzyme inhibitor or angiotensin receptor blocker, CCB calcium channel blocker, SBP systolic blood pressure, DBP diastolic blood pressure. Logistic regression and LASSO regression: Univariate analysis identified symptom-to-door time, diaphoresis, syncope/coma, and admission systolic blood pressure as potential predictors of hypokalemia. Electrocardiographic findings including atrial arrhythmias, ventricular arrhythmias, U-wave presence, prolonged P-wave duration, and extended PR interval emerged as potential risk factors（Table 2）. Incorporate basic demographic factors (age, gender) and select variables with p values less than 0.10 from single factors: Symptom-to-door time, diaphoresis, syncope/coma, systolic blood pressure, atrial arrhythmia, ventricular arrhythmia, U-wave presence, P-wave duration, and PR interval. Using 10-fold cross-validation (nlambda=100), LASSO regression identified six variables with non-zero coefficients at the optimal penalty parameter (λ.1se=0.054): Symptom-to-door time, systolic blood pressure, syncope/coma, ventricular arrhythmia, PR interval, and U-wave presence（Figure 1）. The six LASSO-selected variables were incorporated into a multivariate logistic regression model with hypokalemia as the outcome variable. Variable selection was performed using backward stepwise regression, with α=0.05 as the exclusion threshold. The final model comprised five independent predictors: shorter symptom-to-door time (OR=0.85), syncope/coma (OR=3.57), atrial arrhythmias (OR=4.18), prolonged PR interval (OR=1.01), and U-wave presence (OR=5.20)(Table 3）. Table 2 . Univariate Logistic Regression Analysis for Predictors of Hypokalemia Variables Event N OR 95% CI p -value Symptom-to-door time 114 0.86 0.79, 0.94 0.001 Diaphoresis 82 1.69 1.03, 2.78 0.037 Syncope/Coma 9 2.86 0.99, 8.24 0.052 SBP 114 0.99 0.98, 1.00 0.023 P-wave duration 114 1.02 1.00, 1.05 0.044 PR interval 114 1.01 1.00, 1.02 0.009 U wave present 31 4.44 2.30, 8.55 <0.001 Atrial arrhythmia 11 4.29 1.45, 12.69 0.008 Ventricular arrhythmia 2 0.27 0.06, 1.20 0.084 Analyses using logistic regression models OR odds ratio, 95% CI 95% confidence interval, SBP systolic blood pressure Figure 1 . The least absolute contraction and selection operator (LASSO) logistic regression is used for feature selection. On the left, the LASSO coefficient path diagram of 9 influencing factors is shown, and on the right, the 10-fold cross-validation curve is shown. Table 3 . Multivariate logistic regression analysis of hypokalemia in STEMI patients Characteristic N Event N OR 95% CI p-value Symptom-to-door time 320 114 0.85 0.78, 0.94 0.002 Syncope/Coma 15 9 3.57 1.12, 11.37 0.031 Atrial arrhythmia 16 11 4.18 1.33, 13.17 0.015 PR interval 320 114 1.01 1.00, 1.02 0.019 U wave present 47 31 5.2 2.59, 10.46 <0.001 Analyses using logistic regression models OR odds ratio, 95% CI 95% confidence interval, Establishment of nomogram : Based on 5 influencing factors (symptom-to-door time, syncope/coma, atrial arrhythmia, PR interval, U-wave) selected by multiple logistic regression were entered into the joint prediction model. A nomogram is constructed, showing the values of each independent influencing factor and the corresponding scores of each influencing factor. The scores of each influencing factor are added up to obtain the total score, and its corresponding risk level is the probability of predicting the occurrence of hypokalemia (Figure 2). Figure 2 . Nomogram of multi-factor joint prediction model for hypokalemia in STEMI patients Prediction Model Evaluation: Perform ROC analysis on the joint prediction model, and the AUC was 0.735 with a 95% confidence interval of 0.680-0.791.（Figure 3）. Internal validation with 1000 bootstrap resamples demonstrated robust model performance, yielding a corrected C-statistic of 0.700. This indicates preferable discrimination and calibration. The calibration curve (Figure 4) indicates that there is good consistency between the model prediction and the actual observation results, proving the superiority of the fitted model. The DCA curve results (Figure 4) indicate that when the threshold probability is within the range of 10% to 80%, applying the model to guide clinical decision-making within this range has good clinical benefits. (Figure 5). Figure 3 . ROC curve of prediction model of hypokalemia in STEMI patient Figure 4. Calibration curve of multivariate joint prediction model for hypokalemia in STEMI patients Figure 5. DCA curve of multi-factor joint prediction model for hypokalemia in STEMI patients Discussion This study focuses on pre-hospital emergency scenarios and constructs and validates a hypokalemia risk prediction model practical, concise variables, and good predictive performance. Using LASSO and multivariate logistic regression, we identified five key predictors of prehospital hypokalemia: symptom-to-door time, syncope/coma, atrial arrhythmias, PR interval prolongation, and U-wave presence. The significance of these predictive factors lies in their ability to provide early signals of hypokalemia. This is easily overlooked in acute STEMI attacks. For example, the Symptom-to-door time and the presence of syncope or coma reflect the urgency and potential severity of the patient's condition. Atrial arrhythmia, prolonged PR interval, and U-wave are signs of electrophysiological disorders in patients. Based on these factors, we constructed a nomogram. Our prediction model achieved an AUC of 0.735 (95% CI: 0.680–0.791) on ROC analysis, indicating reasonably strong discriminative ability. Notably, manifestations such as muscle weakness, U-wave appearance, and arrhythmias are common to both hypokalemia and acute myocardial infarction, complicating early differentiation[8]. The prediction model, built upon five convenient and easily accessible variables, demonstrated robust performance. This was substantiated by a calibration curve showing strong agreement between predictions and observations. Specifically designed for early hypokalemia detection in STEMI, the model's clinical value was quantified by decision curve analysis, which indicated significant net benefits over a wide spectrum of threshold probabilities. This suggests its potential to guide clinicians in making superior decisions regarding potassium supplementation, with the ultimate goal of lowering arrhythmia-related mortality and enhancing patient outcomes. Previous retrospective studies consistently demonstrate that STEMI patients frequently develop hypokalemia within 12 hours of onset, with associated increases in malignant arrhythmias and in-hospital mortality [9,10]. This study also found that early onset hypokalemia can occur in STEMI patients. This phenomenon likely reflects acute stress responses characterized by sympathetic nervous system activation, massive catecholamine release, and β2-receptor-mediated potassium shifts into cells. Consequently, prehospital providers must maintain high suspicion for electrolyte disturbances when evaluating patients with suspected acute coronary syndromes, especially for high-risk patients with symptom onset<2 hours, blood potassium assessment and intervention should be prioritized. Syncope is one of the important atypical manifestations of STEMI patients, and some STEMI patients present with syncope as the initial symptom rather than atypical chest pain, which can be easily misdiagnosed or delayed in treatment [11]. In our cohort, syncope/coma emerged as a robust predictor of hypokalemia (OR=3.57). STEMI itself can cause abnormal blood potassium levels through the release of potassium from necrotic myocardium, stress response, and impaired renal function, which can directly lead to fatal arrhythmias, resulting in syncope/coma. In addition, low potassium itself can also cause a decrease in neuromuscular excitability, which can manifest as muscle weakness, respiratory depression, and even consciousness disorders. Pre-hospital identification of such symptoms should be highly alert to the occurrence of electrolyte imbalances. Atrial arrhythmias (premature atrial contractions and transient atrial tachycardia) represent both important clinical manifestations of hypokalemia and independent predictors in our analysis (OR=4.18). Hypokalemia can significantly prolong the duration of atrial action potential by inhibiting fast delayed rectifier potassium current (IKr) and fast delayed rectifier potassium current (IKur) on the atrial muscle cell membrane, and ultimately triggering atrial arrhythmia [12]. The pig acute myocardial infarction model constructed by Bikou et al. showed that within 2 hours after myocardial infarction, after using some mechanical devices to replace left ventricular function, and the incidence of atrial arrhythmias also decreased, confirming that "mechanical electrical feedback" is a reversible arrhythmogenic factor [13]. At the clinical level, although the incidence of atrial arrhythmia in STEMI patients is lower than that of ventricular arrhythmia, its value as an early signal of hypokalemia cannot be ignored. A study based on the risk of atherosclerosis in the community found that the incidence of atrial arrhythmias events in the population with coronary heart disease and hypokalemia was as high as 19.84%[14]. We hypothesize a self-perpetuating cycle in STEMI: ischemic-driven catecholamine release lowers serum potassium, which facilitates atrial arrhythmias through altered depolarization/repolarization. These arrhythmias, compounded by ischemia-induced atrial stretch, further impair hemodynamics and coronary perfusion, worsening ischemia and perpetuating hypokalemia. This electromechanical vicious cycle underscores that new atrial arrhythmias warrant prompt potassium evaluation and correction. The prolongation of PR interval reflects the delay of atrioventricular node conduction. Hypokalemia delays atrioventricular conduction through multiple mechanisms, including sodium-potassium pump inhibition and reduced resting membrane potential. A case report of severe hypokalemia (1.31 mmol/L) confirmed that the prolongation of PR interval after potassium supplementation is reversible [15]. Therefore, detecting prolonged PR interval in STEMI patients can serve as a warning signal for hypokalemia, and the recovery of PR interval after potassium supplementation can also serve as an effective indicator of potassium supplementation. It has strong operability and repeatability in pre-hospital emergency care. Zareei et al. found in 248 patients with acute coronary syndrome that PR interval prolongation can serve as a potential marker of cardiac structure/ischemic load[16]. However, further research is needed to determine whether the PR interval can directly reflect ischemia in the atrioventricular node of STEMI patients. U-waves represent a hallmark electrocardiographic marker of hypokalemia. When hypokalemia occurs, the outward potassium current (such as IKr, Ito) of myocardial cells weakens, resulting in asynchronous repolarization between ventricular myocardium and conduction system, thus forming U-waves [17]. Ramadurai et al. found that the incidence of U-waves significantly increased with the severity of hypokalemia [18]. It is worth noting that the clinical significance of U-waves seems to be not limited to electrolyte imbalance. Inverted U-waves may represent an early, non-invasive marker of acute myocardial ischemia, even in the absence of canonical ST-T changes, as evidenced by a case study from Girish et al. [19]. In our study, U-waves were confirmed to be an independent predictor of prehospital hypokalemia in STEMI patients (OR=5.2, 95% CI: 2.59-10.46, p<0.001). The appearance of U-waves may not only reflect repolarization abnormalities caused by low potassium, but may also be combined with myocardial electrophysiological disorders caused by ischemia. However, U-waves can also occur in some healthy individuals such as athletes and those with bradycardia, so the quantitative relationship between U-wave morphology (positive/negative, amplitude, duration) and blood potassium levels, as well as the degree of coronary artery disease, still needs further exploration in the future. A recently published large-scale retrospective cohort study based on the MIMIC-IV database found a significant positive correlation between serum potassium fluctuations and in-hospital mortality [20]. The study by Fax é n et al. demonstrated a positive correlation between hypokalemia upon admission and adverse events during hospitalization [21]. Petnak et al. found that low blood potassium at discharge is significantly associated with one-year mortality [22]. These pieces of evidence all indicate that hypokalemia is an independent risk factor for death in STEMI patients, and its electrophysiological mechanism may be that after myocardial infarction occurs, the expression and function of potassium channels in myocardial cells undergo remodeling downregulation, leading to prolonged action potential duration and increased repolarization dispersion. This electrical remodeling itself puts the myocardium in a vulnerable state, and the presence of hypokalemia can further inhibit potassium channel function, weaken the ischemic preconditioning protective effect mediated by potassium ion channels, aggravate calcium influx and intracellular calcium overload, thereby inducing or exacerbating arrhythmia and increasing mortality [23]. Although the significance of in-hospital potassium management has been well documented, far less attention has been paid to the early, prehospital detection of hypokalemia. Our study bridges this gap by integrating five straightforward clinical and electrocardiographic parameters into a nomogram. This approach facilitates rapid risk assessment at the bedside, significantly enhancing its utility in emergent prehospital settings. Study limitations Although this study has made positive explorations in model construction and validation, there are still limitations. First, as a single-center retrospective study, selection and information biases cannot be entirely eliminated. Future multicenter prospective cohort studies are warranted to validate the model's generalizability and stability. Second, our analysis focused on admission potassium levels, limiting insights into temporal potassium dynamics. Future investigations incorporating continuous potassium monitoring through time-to-event models (e.g., Cox regression, Landmark analysis) would better characterize temporal patterns of hypokalemia development. Third, this study still lacks some important predictive variables, such as the Global Acute Coronary Event Registry (GRACE) risk score and Killip classification. In the future, randomized controlled trials can be designed to evaluate whether model guided preventive potassium supplementation strategies can reduce the incidence and mortality of arrhythmias, thus achieving a closed-loop from prediction to intervention. Conclusion We developed a practical and validated nomogram for predicting prehospital hypokalemia in STEMI patients using five easily obtainable clinical and ECG variables. This tool may facilitate early identification and intervention in high-risk individuals, potentially improving prehospital management and clinical outcomes. Declarations Author Contribution All authors have made substantial contributions to this work. Specifically: Qian Gu took the lead in drafting the manuscript and its subsequent revisions. Chao Tang was responsible for data analysis and provided critical feedback on the initial draft. Fei Shi and Baojun Yang contributed to the data collection and investigation. Xiao song Gu and Jing Zhu contributed to the study design, project supervision, and funding acquisition. All authors reviewed and approved the final version. References Weiss JN, Qu Z, Shivkumar K. Electrophysiology of hypokalemia and hyperkalemia. Circ Arrhythm Electrophysiol. 2017;10:e004667. https://doi.org/10.1161/CIRCEP.116.004667 . Wang Y, Li Q, Tao B, Angelini M, Ramadoss S, Sun B, et al. Fibroblasts in heart scar tissue directly regulate cardiac excitability and arrhythmogenesis. Science. 2023;381:1480–7. https://doi.org/10.1126/science.adh9925 . Colombo MG, Kirchberger I, Amann U, Dinser L, Meisinger C. Association of serum potassium concentration with mortality and ventricular arrhythmias in patients with acute myocardial infarction: A systematic review and meta-analysis. Eur J Prev Cardiol. 2018;25:576–95. https://doi.org/10.1177/2047487318759694 . Xi H, Yu R-H, Wang N, Chen X-Z, Zhang W-C, Hong T. Serum potassium levels and mortality of patients with acute myocardial infarction: A systematic review and meta-analysis of cohort studies. Eur J Prev Cardiol. 2019;26:145–56. https://doi.org/10.1177/2047487318780466 . Ravn Jacobsen M, Jabbari R, Glinge C, Kjær Stampe N, Butt JH, Blanche P, et al. Potassium disturbances and risk of ventricular fibrillation among patients with ST-segment–elevation myocardial infarction. J Am Heart Assoc. 2020;9:e014160. https://doi.org/10.1161/JAHA.119.014160 . Lawton JS, Tamis-Holland JE, Bangalore S, Bates ER, Beckie TM, Bischoff JM, et al. 2021 ACC/AHA/SCAI guideline for coronary artery revascularization: A report of the American college of cardiology/american heart association joint committee on clinical practice guidelines. Circulation. 2022;145. https://doi.org/10.1161/CIR.0000000000001038 . Thygesen K, Alpert JS, Jaffe AS, Chaitman BR, Bax JJ, Morrow DA et al. Fourth Universal Definition of Myocardial Infarction (2018). Circulation 2018;138. https://doi.org/10.1161/CIR.0000000000000617 Kardalas E, Paschou SA, Anagnostis P, Muscogiuri G, Siasos G, Vryonidou A. Hypokalemia: A clinical update. Endocr Connect. 2018;7:R135–46. https://doi.org/10.1530/EC-18-0109 . Jensen CJ, Nielsen JK, Talbott MM, O’Connell D, Patel VS, Armstrong PA, et al. Effects of serum potassium on mortality in patients with ST-elevation myocardial infarction. Cureus. 2024. https://doi.org/10.7759/cureus.61126 . Uluganyan M, Ekmekci A, Murat A, Avsar S, Ulutas TK, Uyarel H, et al. Admission serum potassium level is associated with in-hospital and long-term mortality in ST-elevation myocardial infarction. Anatol J Cardiol. 2015. https://doi.org/10.5152/akd.2015.5706 . Sato N, Minami Y, Ako J, Maeda A, Akashi Y, Ikari Y, et al. Clinical significance of prehospital 12-lead electrocardiography in patients with ST-segment elevation myocardial infarction presenting with syncope: From a multicenter observational registry (K-ACTIVE study). Heart Vessels. 2021;36:1466–73. https://doi.org/10.1007/s00380-021-01832-z . Tazmini K, Frisk M, Lewalle A, Laasmaa M, Morotti S, Lipsett DB, et al. Hypokalemia promotes arrhythmia by distinct mechanisms in atrial and ventricular myocytes. Circ Res. 2020;126:889–906. https://doi.org/10.1161/CIRCRESAHA.119.315641 . Bikou O, Kho C, Ishikawa K. Atrial stretch and arrhythmia after myocardial infarction. Aging. 2018;11:11–2. https://doi.org/10.18632/aging.101729 . Wu Y, Kong X-J, Ji Y-Y, Fan J, Ji C-C, Chen X-M, et al. Serum electrolyte concentrations and risk of atrial fibrillation: An observational and mendelian randomization study. BMC Genomics. 2024;25:280. https://doi.org/10.1186/s12864-024-10197-2 . Wang X, Han D, Li G. Electrocardiographic manifestations in severe hypokalemia. J Int Med Res. 2020;48:0300060518811058. https://doi.org/10.1177/0300060518811058 . Zareei M, Zareiamand H, Kamali M, Ardalani N, Ebrahimi A, Nabati M. Can prolonged P-R interval predict clinical outcomes in non-ST elevation acute coronary syndrome patients? BMC Cardiovasc Disord. 2024;24:137. https://doi.org/10.1186/s12872-024-03809-y . Kihlgren M, Almqvist C, Amankhani F, Jonasson L, Norman C, Perez M, et al. The U-wave: A remaining enigma of the electrocardiogram. J Electrocardiol. 2023;79:13–20. https://doi.org/10.1016/j.jelectrocard.2023.03.001 . Ramadurai S, Varadarajan V, Harini SS, T T, Varadarajan VK. Electrocardiographic changes in patients with hypokalemia and their correlation with serum potassium levels. Cureus. 2025. https://doi.org/10.7759/cureus.91922 . Mp G, Gupta MD, Mukhopadhyay S, Yusuf J, Tn SR. U wave: An important noninvasive electrocardiographic diagnostic marker. Indian Pacing Electrophysiol J 2005. Zhou Y, Chen Y, Liang S, Li Y, Zhao C, Wu Z. Association between potassium fluctuation and in-hospital mortality in acute myocardial infarction patients: a retrospective analysis of the MIMIC‐IV database. Clin Res Cardiol. 2025. https://doi.org/10.1007/s00392-025-02613-8 . Faxén J, Xu H, Evans M, Jernberg T, Szummer K, Carrero J-J. Potassium levels and risk of in-hospital arrhythmias and mortality in patients admitted with suspected acute coronary syndrome. Int J Cardiol. 2019;274:52–8. https://doi.org/10.1016/j.ijcard.2018.09.099 . Thongprayoon C, Cheungpasitporn W, Thirunavukkarasu S, Petnak T, Chewcharat A, Bathini T, et al. Serum Potassium Levels at Hospital Discharge and One-Year Mortality among Hospitalized Patients. Med (Mex). 2020;56:236. https://doi.org/10.3390/medicina56050236 . Song T, Hui W, Huang M, Guo Y, Yu M, Yang X, et al. Dynamic changes in ion channels during myocardial infarction and therapeutic challenges. Int J Mol Sci. 2024;25:6467. https://doi.org/10.3390/ijms25126467 . 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University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Tang","suffix":""},{"id":567014607,"identity":"ee551c45-7ddb-4910-b740-0cf58d7a669a","order_by":2,"name":"Bao jun Yang","email":"","orcid":"","institution":"Dushu Lake Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bao","middleName":"jun","lastName":"Yang","suffix":""},{"id":567014608,"identity":"18b4ad75-7a5d-4a10-807c-9051247c3c76","order_by":3,"name":"Fei Shi","email":"","orcid":"","institution":"Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Shi","suffix":""},{"id":567014609,"identity":"0f6688a3-0645-4a25-84e8-fd167761e6ec","order_by":4,"name":"Xiao song Gu","email":"","orcid":"","institution":"Second Affiliated Hospital of Soochow 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16:38:55","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":114448,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8185679/v1/07ce5bb6d5b13972105fb4f1.html"},{"id":99321267,"identity":"2aede0cd-33a5-4b52-af9b-97198dd7148c","added_by":"auto","created_at":"2025-12-31 16:39:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89833,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe least absolute contraction and selection operator (LASSO) logistic regression is used for feature selection. On the left, the LASSO coefficient path diagram of 9 influencing factors is shown, and on the right, the 10-fold cross-validation curve is shown.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8185679/v1/22a870b51783171b0a84fb7c.png"},{"id":99320905,"identity":"4b3b785b-a98f-4521-adc4-008bc15e92ef","added_by":"auto","created_at":"2025-12-31 16:38:56","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47960,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram of multi-factor joint prediction model for hypokalemia in STEMI patients\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8185679/v1/13b6d3b9fd719ec9b819c119.jpeg"},{"id":99320783,"identity":"fd49bea7-eda9-4b51-8ead-ca5e8e396b5b","added_by":"auto","created_at":"2025-12-31 16:38:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13938,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve of prediction model of hypokalemia in STEMI patients\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8185679/v1/9e176908babd2d1287b39391.png"},{"id":99292312,"identity":"166b645a-1809-43b8-9971-3e161fe31a8d","added_by":"auto","created_at":"2025-12-31 10:43:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":32742,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curve of multivariate joint prediction model for hypokalemia in STEMI patients\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8185679/v1/d81756bcccbe1f68fc22e5db.png"},{"id":99319945,"identity":"0acfdafe-e66e-43eb-b746-2c5bd9ee5f49","added_by":"auto","created_at":"2025-12-31 16:38:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":20457,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDCA curve of multi-factor joint prediction model for hypokalemia in STEMI patients\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8185679/v1/cf1274ddcc4e996d16865047.png"},{"id":106399448,"identity":"10a9fc81-1b75-44aa-889a-c5dbefa9bfd6","added_by":"auto","created_at":"2026-04-08 08:29:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1496213,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8185679/v1/e19c7c60-a021-4961-a90e-cf68ee958bae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prehospital Prediction of Hypokalemia in patients with ST‑Segment Elevation Myocardial Infarction: Development and Validation of a Prediction Model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeart function fundamentally depends on the precise generation and propagation of action potentials, in which potassium channels play a crucial role. Hypokalemia is defined as a serum potassium level below 3.5 mmol/L, which severely disrupts the stability of cardiac electrophysiology and leads to the occurrence of arrhythmia, especially during acute myocardial ischemia[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. After myocardial infarction, fibroblasts establish electrical coupling with surviving myocardial cells, causing abnormal changes in potassium channels and inducing arrhythmia[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Multiple clinical studies have also confirmed that abnormal potassium ion concentration significantly increases the risk of malignant arrhythmia and affects patient prognosis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The risk of arrhythmia can be significantly heightened in MI patients with hypokalemia even before revascularization is performed[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHypokalemia patient may present with symptoms such as weakness, nausea, vomiting, coma, or syncope\u0026mdash;manifestations that can overlap with those of myocardial infarction. In clinical practice, it is not accurate to identify concurrent hypokalemia in ST-segment elevation myocardial infarction (STEMI) patients based solely on single symptoms or physical signs. The diagnosis of hypokalemia often relies on the detection of venous potassium levels. The unique susceptibility of cardiomyocytes to extensive damage from even brief ischemia means that patients may already be in the catheterization lab for revascularization before their hypokalemia is even identified. The electrophysiological instability induced by PCI and ischemia-reperfusion injury, aggravated by hypokalemia, results in a markedly increased susceptibility to and higher incidence of arrhythmias[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough there have been studies exploring the relationship between blood potassium levels and STEMI prognosis, most research has focused on in-hospital blood potassium monitoring, and there is relatively little research on predicting and intervening in pre-hospital blood potassium levels. If the high-risk population for hypokalemia can be quickly identified before or in the early stages of admission, and targeted interventions (such as preventive potassium supplementation) can be implemented, it may effectively reduce the incidence of malignant arrhythmias and improve patient prognosis. However, there is currently a lack of multi-indicator combination hypokalemia prediction models suitable for pre-hospital environments.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to retrospectively analyze the clinical data of STEMI patients, explore independent risk factors for prehospital hypokalemia, construct and validate a hypokalemia prediction model suitable for prehospital emergency scenarios, provide scientific basis for early identification of high-risk patients, optimize prehospital management strategies, and ultimately achieve the advancement and integration of the STEMI treatment chain.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eA retrospective observational study was conducted to collect cases of STEMI patients who were admitted to the emergency room of the Second Affiliated Hospital of Soochow University from January 2023 to December 2024. Inclusion criteria comprised: ①The diagnosis of patients with STEMI meets the diagnostic criteria of the 4th edition of the Global definition of myocardial infarction (2018)[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]; ②Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, electrocardiogram-confirmed STEMI, and primary percutaneous coronary intervention with stent deployment within 12 hours of symptom onset;③ Intravenous blood sampling for serum potassium completed within 1 hour of emergency department arrival;④Capable of communication, without psychiatric disorder, and able to provide accurate and valid clinical information. Exclusion criteria were as follows: ① Patients with incomplete clinical data (e.g., missing ECG or serum potassium records);② Presence of atrial fibrillation, atrial flutter, or any atrioventricular block on the emergency ECG, precluding reliable interpretation of PR intervals and P waves༛③ Administration of potassium supplementation or medications significantly altering potassium homeostasis before hospital admission༛④ Receipt of life-sustaining measures (e.g., endotracheal intubation, extracorporeal membrane oxygenation) prior to blood draw, or patients who died prior to hospital arrival༛⑤End-stage renal disease requiring maintenance hemodialysis༛⑥Active chemotherapy or radiotherapy for malignancy༛\u003c/p\u003e \u003cp\u003eReview and collect clinical data of patients with acute STEMI, including past disease history, family history, medication history, smoking history, and alcohol consumption history. The basic data of patients at the time of emergency reception, including gender, age, complications, risk factors (such as smoking, drinking, diabetes, hypertension, family history of cardiovascular and cerebrovascular diseases), cardiac and non-cardiac symptoms (such as chest pain, dyspnea, diaphoresis, vomiting, fatigue, etc.), onset to hospital time, pre hospital vital signs (blood pressure, heart rate), and emergency ECG parameters (heart rate, P-wave duration, PR interval, QRS duration, QT/QTc, U-wave, arrhythmia, infarction site, etc.). Arrhythmias include ventricular arrhythmias (such as premature ventricular contractions, transient ventricular tachycardia, etc.) and atrial arrhythmias (such as premature atrial contractions, atrial tachycardia etc.).\u003c/p\u003e \u003cp\u003eAll patients completed venous potassium collection within 1 hour of arrival at the emergency room, and concentrations were measured by direct ion-selective electrode with a reference interval of 3.5\u0026ndash;5.1 mmol/l.\u003c/p\u003e \u003cp\u003eThis study divided patients into two groups based on their blood potassium levels. One group was the hypokalemia group (Blood potassium level is less than 3.5 mmol/L), with a total of 114 cases, including 101 males and 13 females; the other group was the non-hypokalemia group (Blood potassium level is above than 3.5 mmol/L), with a total of 206 cases, including 172 males and 34 females.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the Second Affiliated Hospital of Soochow University (Approval No. JD-HG-2025071). Due to the retrospective nature of the study, the need for informed consent was waived by the Ethics Committee of the Second Affiliated Hospital of Soochow University. The study was performed in accordance with the ethical standards as laid down in the\u0026nbsp;Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using SPSS version 27.0 and R software (version 4.2.2). Normality was assessed using the Shapiro-Wilk test. Normally distributed variables are presented as mean ± standard deviation, Independent sample t-test is used for inter group comparison. Non-normally distributed variables were expressed as median (interquartile range), with between-group comparisons performed using the Wilcoxon rank-sum test. Categorical variables are presented as counts and percentages (n/%), with between-group comparisons conducted using χ² test or Fisher's exact test as appropriate. Screening independent influencing factors of hypokalemia through univariate logistic regression, least absolute shrinkage and selection operator（LASSO）, and multivariate logistic regression. Construct a diagnostic prediction model for prehospital hypokalemia in STEMI patients based on the independent influencing factors obtained and visualize it in a nomogram. Evaluate the predictive model from three aspects: discrimination, calibration, and clinical effectiveness. The discriminability is evaluated by plotting the ROC curve and calculating the area under the curve (AUC), the model calibration is validated by the calibration curve, and the clinical effectiveness of the model is evaluated using the clinical decision curve (DCA). Using Bootstrap method for internal validation of the model to obtain corrected AUC.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline characteristics revealed no significant differences in age, gender, BMI, or medical history between hypokalemic and non-hypokalemic patients. The distribution of infarct-related coronary territories did not differ significantly between the hypokalemic and non-hypokalemic cohorts. Clinically, hypokalemic patients presented with shorter symptom-to-door time (median 2.0 [IQR 1.0-3.0] vs. 2.0 [IQR 1.0-5.0] hours, p\u0026lt;0.001), lower admission systolic blood pressure (134\u0026plusmn;30 vs. 142\u0026plusmn;28 mmHg, p=0.025), and higher rates of diaphoresis and syncope/coma. Electrocardiographic analysis revealed significantly longer PR intervals in hypokalemic patients (175 [IQR 155-188] vs. 165 [IQR 151-180] ms, p=0.006), with higher prevalence of atrial arrhythmias and U-wave presence.（\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e）\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;. Comparisons of characteristics between hypokalemia and non-hypokalemia\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHypokalemia group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNon-hypokalemia group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eN = 320\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eN = 206\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eN = 114\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003epotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.69 (3.40, 3.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.32 (3.16, 3.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.90 (3.72, 4.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23,484.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasic information\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58 (48, 68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58 (44, 69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58 (52, 65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11,627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI\u0026nbsp;, kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.1 (22.5, 27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.9 (22.2, 27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.1 (22.8, 27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10,577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e273 (85.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e172 (83.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e101 (88.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e183 (57.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e117 (56.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66 (57.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical and medication history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e201 (62.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e132 (64.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69 (60.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70 (21.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrior MI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCerebrovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eACEI/ARB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60 (18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42 (20.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78 (24.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56 (27.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22 (19.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBeta-blocker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33 (10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (10.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInsulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMetformin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27 (8.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (8.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (7.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiuretics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease situation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSymptom-to-door time, h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.0 (1.0, 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.0 (1.0, 5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.0 (1.0, 3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14,582.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e139 \u0026plusmn; 29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e142 \u0026plusmn; 28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e134 \u0026plusmn; 30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e87 (72, 101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88 (74, 101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85 (71, 100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12,647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVomiting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47 (22.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiaphoresis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e114 (35.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82 (39.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 (28.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSyncope/Coma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (7.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eElectrocardiogram parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHeart rate, bpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75 (65, 87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75 (65, 85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e74 (62, 91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11,705.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP-wave duration, ms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93 (87, 102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93 (86, 100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95 (89, 105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10,157.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePR-interval, ms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e169 (152, 183)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e165 (151, 180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e175 (155, 188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9,582.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQRS duration, ms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e98 (90, 104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e97 (91, 103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e98 (90, 104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11,465.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQT interval, ms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e367 (344, 395)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e367 (345, 393)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e368 (343, 401)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11,584.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQTc interval, ms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e409 (392, 430)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e408 (390, 430)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e410 (395, 431)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11,108.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAtrial arrhythmia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVentricular arrhythmia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eU wave present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47 (14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31 (27.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMyocardial infarction site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExtensive anterior wall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63 (19.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37 (18.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26 (22.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnterior wall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e122 (38.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78 (37.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44 (38.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLateral wall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e121 (37.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80 (38.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41 (36.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePosterior wall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\n \u003cp\u003eContinuous data are presented as mean \u0026plusmn; SD or median (Q1, Q3); categorical data are presented as count (%).P values are derived from t tests for continuous variables and chi-square tests for categorical variables.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBMI body mass index, ACEI/ARB angiotensin-converting enzyme inhibitor or angiotensin receptor blocker, CCB calcium channel blocker, SBP systolic blood pressure, DBP diastolic blood pressure.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eLogistic regression and LASSO regression:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate analysis identified symptom-to-door time, diaphoresis, syncope/coma, and admission systolic blood pressure as potential predictors of hypokalemia. Electrocardiographic findings including atrial arrhythmias, ventricular arrhythmias, U-wave presence, prolonged P-wave duration, and extended PR interval emerged as potential risk factors（Table 2）. Incorporate basic demographic factors (age, gender) and select variables with p values less than 0.10 from single factors: Symptom-to-door time, diaphoresis, syncope/coma, systolic blood pressure, atrial arrhythmia, ventricular arrhythmia, U-wave presence, P-wave duration, and PR interval. Using 10-fold cross-validation (nlambda=100), LASSO regression identified six variables with non-zero coefficients at the optimal penalty parameter (\u0026lambda;.1se=0.054): Symptom-to-door time, systolic blood pressure, syncope/coma, ventricular arrhythmia, PR interval, and U-wave presence（Figure 1）. The six LASSO-selected variables were incorporated into a multivariate logistic regression model with hypokalemia as the outcome variable. Variable selection was performed using backward stepwise regression, with \u0026alpha;=0.05 as the exclusion threshold. The final model comprised five independent predictors: shorter symptom-to-door time (OR=0.85), syncope/coma (OR=3.57), atrial arrhythmias (OR=4.18), prolonged PR interval (OR=1.01), and U-wave presence (OR=5.20)(Table 3）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003eUnivariate Logistic Regression Analysis for Predictors of Hypokalemia\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEvent N\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSymptom-to-door time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.79, 0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiaphoresis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03, 2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSyncope/Coma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.99, 8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.98, 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP-wave duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00, 1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePR interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00, 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eU wave present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.30, 8.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAtrial arrhythmia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.45, 12.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVentricular arrhythmia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06, 1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eAnalyses using logistic regression models \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eOR odds ratio, 95% CI 95% confidence interval, SBP systolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e. The least absolute contraction and selection operator (LASSO) logistic regression is used for feature selection. On the left, the LASSO coefficient path diagram of 9 influencing factors is shown, and on the right, the 10-fold cross-validation curve is shown.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e \u003cstrong\u003eMultivariate logistic regression analysis of hypokalemia in STEMI patients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEvent N\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSymptom-to-door time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.78, 0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSyncope/Coma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.12, 11.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAtrial arrhythmia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.33, 13.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePR interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00, 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eU wave present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.59, 10.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\n \u003cp\u003eAnalyses using logistic regression models\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eOR odds ratio, 95% CI 95% confidence interval,\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eEstablishment of nomogram :\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on 5 influencing factors (symptom-to-door time, syncope/coma, atrial arrhythmia, PR interval, U-wave) selected by multiple logistic regression were entered into the joint prediction model. A nomogram is constructed, showing the values of each independent influencing factor and the corresponding scores of each influencing factor. The scores of each influencing factor are added up to obtain the total score, and its corresponding risk level is the probability of predicting the occurrence of hypokalemia (Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e. Nomogram of multi-factor joint prediction model for hypokalemia in STEMI patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction Model Evaluation:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerform ROC analysis on the joint prediction model, and the AUC was 0.735 with a 95% confidence interval of 0.680-0.791.（Figure 3）. Internal validation with 1000 bootstrap resamples demonstrated robust model performance, yielding a corrected C-statistic of 0.700. This indicates preferable discrimination and calibration. The calibration curve (Figure 4) indicates that there is good consistency between the model prediction and the actual observation results, proving the superiority of the fitted model. The DCA curve results (Figure 4) indicate that when the threshold probability is within the range of 10% to 80%, applying the model to guide clinical decision-making within this range has good clinical benefits. (Figure 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e. ROC curve of prediction model of hypokalemia in STEMI patient\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4. Calibration curve of multivariate joint prediction model for hypokalemia in STEMI patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 5. DCA curve of multi-factor joint prediction model for hypokalemia in STEMI patients\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study focuses on pre-hospital emergency scenarios and constructs and validates a hypokalemia risk prediction model practical, concise variables, and good predictive performance. Using LASSO and multivariate logistic regression, we identified five key predictors of prehospital hypokalemia: symptom-to-door time, syncope/coma, atrial arrhythmias, PR interval prolongation, and U-wave presence. The significance of these predictive factors lies in their ability to provide early signals of hypokalemia. This is easily overlooked in acute STEMI attacks. For example, the Symptom-to-door time and the presence of syncope or coma reflect the urgency and potential severity of the patient's condition. Atrial arrhythmia, prolonged PR interval, and U-wave are signs of electrophysiological disorders in patients. Based on these factors, we constructed a nomogram. Our prediction model achieved an AUC of 0.735 (95% CI: 0.680–0.791) on ROC analysis, indicating reasonably strong discriminative ability. Notably, manifestations such as muscle weakness, U-wave appearance, and arrhythmias are common to both hypokalemia and acute myocardial infarction, complicating early differentiation[8]. The prediction model, built upon five convenient and easily accessible variables, demonstrated robust performance. This was substantiated by a calibration curve showing strong agreement between predictions and observations. Specifically designed for early hypokalemia detection in STEMI, the model's clinical value was quantified by decision curve analysis, which indicated significant net benefits over a wide spectrum of threshold probabilities. This suggests its potential to guide clinicians in making superior decisions regarding potassium supplementation, with the ultimate goal of lowering arrhythmia-related mortality and enhancing patient outcomes.\u003c/p\u003e\n\u003cp\u003ePrevious retrospective studies consistently demonstrate that STEMI patients frequently develop hypokalemia within 12 hours of onset, with associated increases in malignant arrhythmias and in-hospital mortality [9,10]. This study also found that early onset hypokalemia can occur in STEMI patients. This phenomenon likely reflects acute stress responses characterized by sympathetic nervous system activation, massive catecholamine release, and β2-receptor-mediated potassium shifts into cells. Consequently, prehospital providers must maintain high suspicion for electrolyte disturbances when evaluating patients with suspected acute coronary syndromes, especially for high-risk patients with symptom onset\u0026lt;2 hours, blood potassium assessment and intervention should be prioritized.\u003c/p\u003e\n\u003cp\u003eSyncope is one of the important atypical manifestations of STEMI patients, and some STEMI patients present with syncope as the initial symptom rather than atypical chest pain, which can be easily misdiagnosed or delayed in treatment [11]. In our cohort, syncope/coma emerged as a robust predictor of hypokalemia (OR=3.57). STEMI itself can cause abnormal blood potassium levels through the release of potassium from necrotic myocardium, stress response, and impaired renal function, which can directly lead to fatal arrhythmias, resulting in syncope/coma. In addition, low potassium itself can also cause a decrease in neuromuscular excitability, which can manifest as muscle weakness, respiratory depression, and even consciousness disorders. Pre-hospital identification of such symptoms should be highly alert to the occurrence of electrolyte imbalances.\u003c/p\u003e\n\u003cp\u003eAtrial arrhythmias (premature atrial contractions and transient atrial tachycardia) represent both important clinical manifestations of hypokalemia and independent predictors in our analysis (OR=4.18). Hypokalemia can significantly prolong the duration of atrial action potential by inhibiting fast delayed rectifier potassium current (IKr) and fast delayed rectifier potassium current (IKur) on the atrial muscle cell membrane, and ultimately triggering atrial arrhythmia [12]. The pig acute myocardial infarction model constructed by Bikou et al. showed that within 2 hours after myocardial infarction, after using some mechanical devices to replace left ventricular function, and the incidence of atrial arrhythmias also decreased, confirming that \"mechanical electrical feedback\" is a reversible arrhythmogenic factor [13]. At the clinical level, although the incidence of atrial arrhythmia in STEMI patients is lower than that of ventricular arrhythmia, its value as an early signal of hypokalemia cannot be ignored. A study based on the risk of atherosclerosis in the community found that the incidence of atrial arrhythmias events in the population with coronary heart disease and hypokalemia was as high as 19.84%[14]. We hypothesize a self-perpetuating cycle in STEMI: ischemic-driven catecholamine release lowers serum potassium, which facilitates atrial arrhythmias through altered depolarization/repolarization. These arrhythmias, compounded by ischemia-induced atrial stretch, further impair hemodynamics and coronary perfusion, worsening ischemia and perpetuating hypokalemia. This electromechanical vicious cycle underscores that new atrial arrhythmias warrant prompt potassium evaluation and correction.\u003c/p\u003e\n\u003cp\u003eThe prolongation of PR interval reflects the delay of atrioventricular node conduction. Hypokalemia delays atrioventricular conduction through multiple mechanisms, including sodium-potassium pump inhibition and reduced resting membrane potential. A case report of severe hypokalemia (1.31 mmol/L) confirmed that the prolongation of PR interval after potassium supplementation is reversible [15]. Therefore, detecting prolonged PR interval in STEMI patients can serve as a warning signal for hypokalemia, and the recovery of PR interval after potassium supplementation can also serve as an effective indicator of potassium supplementation. It has strong operability and repeatability in pre-hospital emergency care. Zareei et al. found in 248 patients with acute coronary syndrome that PR interval prolongation can serve as a potential marker of cardiac structure/ischemic load[16]. However, further research is needed to determine whether the PR interval can directly reflect ischemia in the atrioventricular node of STEMI patients.\u003c/p\u003e\n\u003cp\u003eU-waves represent a hallmark electrocardiographic marker of hypokalemia. When hypokalemia occurs, the outward potassium current (such as IKr, Ito) of myocardial cells weakens, resulting in asynchronous repolarization between ventricular myocardium and conduction system, thus forming U-waves [17]. Ramadurai et al. found that the incidence of U-waves significantly increased with the severity of hypokalemia [18]. It is worth noting that the clinical significance of U-waves seems to be not limited to electrolyte imbalance. Inverted U-waves may represent an early, non-invasive marker of acute myocardial ischemia, even in the absence of canonical ST-T changes, as evidenced by a case study from Girish et al. [19]. In our study, U-waves were confirmed to be an independent predictor of prehospital hypokalemia in STEMI patients (OR=5.2, 95% CI: 2.59-10.46, p\u0026lt;0.001). The appearance of U-waves may not only reflect repolarization abnormalities caused by low potassium, but may also be combined with myocardial electrophysiological disorders caused by ischemia. However, U-waves can also occur in some healthy individuals such as athletes and those with bradycardia, so the quantitative relationship between U-wave morphology (positive/negative, amplitude, duration) and blood potassium levels, as well as the degree of coronary artery disease, still needs further exploration in the future.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA recently published large-scale retrospective cohort study based on the MIMIC-IV database found a significant positive correlation between serum potassium fluctuations and in-hospital mortality [20]. The study by Fax é n et al. demonstrated a positive correlation between hypokalemia upon admission and adverse events during hospitalization [21]. Petnak et al. found that low blood potassium at discharge is significantly associated with one-year mortality [22]. These pieces of evidence all indicate that hypokalemia is an independent risk factor for death in STEMI patients, and its electrophysiological mechanism may be that after myocardial infarction occurs, the expression and function of potassium channels in myocardial cells undergo remodeling downregulation, leading to prolonged action potential duration and increased repolarization dispersion. This electrical remodeling itself puts the myocardium in a vulnerable state, and the presence of hypokalemia can further inhibit potassium channel function, weaken the ischemic preconditioning protective effect mediated by potassium ion channels, aggravate calcium influx and intracellular calcium overload, thereby inducing or exacerbating arrhythmia and increasing mortality [23].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough the significance of in-hospital potassium management has been well documented, far less attention has been paid to the early, prehospital detection of hypokalemia. Our study bridges this gap by integrating five straightforward clinical and electrocardiographic parameters into a nomogram. This approach facilitates rapid risk assessment at the bedside, significantly enhancing its utility in emergent prehospital settings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough this study has made positive explorations in model construction and validation, there are still limitations. First, as a single-center retrospective study, selection and information biases cannot be entirely eliminated. Future multicenter prospective cohort studies are warranted to validate the model's generalizability and stability. Second, our analysis focused on admission potassium levels, limiting insights into temporal potassium dynamics. Future investigations incorporating continuous potassium monitoring through time-to-event models (e.g., Cox regression, Landmark analysis) would better characterize temporal patterns of hypokalemia development. Third, this study still lacks some important predictive variables, such as the Global Acute Coronary Event Registry (GRACE) risk score and Killip classification. In the future, randomized controlled trials can be designed to evaluate whether model guided preventive potassium supplementation strategies can reduce the incidence and mortality of arrhythmias, thus achieving a closed-loop from prediction to intervention.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe developed a practical and validated nomogram for predicting prehospital hypokalemia in STEMI patients using five easily obtainable clinical and ECG variables. This tool may facilitate early identification and intervention in high-risk individuals, potentially improving prehospital management and clinical outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors have made substantial contributions to this work. Specifically: Qian Gu took the lead in drafting the manuscript and its subsequent revisions. Chao Tang was responsible for data analysis and provided critical feedback on the initial draft. Fei Shi and Baojun Yang contributed to the data collection and investigation. Xiao song Gu and Jing Zhu contributed to the study design, project supervision, and funding acquisition. All authors reviewed and approved the final version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWeiss JN, Qu Z, Shivkumar K. Electrophysiology of hypokalemia and hyperkalemia. Circ Arrhythm Electrophysiol. 2017;10:e004667. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIRCEP.116.004667\u003c/span\u003e\u003cspan address=\"10.1161/CIRCEP.116.004667\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Li Q, Tao B, Angelini M, Ramadoss S, Sun B, et al. Fibroblasts in heart scar tissue directly regulate cardiac excitability and arrhythmogenesis. Science. 2023;381:1480\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.adh9925\u003c/span\u003e\u003cspan address=\"10.1126/science.adh9925\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColombo MG, Kirchberger I, Amann U, Dinser L, Meisinger C. Association of serum potassium concentration with mortality and ventricular arrhythmias in patients with acute myocardial infarction: A systematic review and meta-analysis. Eur J Prev Cardiol. 2018;25:576\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/2047487318759694\u003c/span\u003e\u003cspan address=\"10.1177/2047487318759694\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXi H, Yu R-H, Wang N, Chen X-Z, Zhang W-C, Hong T. Serum potassium levels and mortality of patients with acute myocardial infarction: A systematic review and meta-analysis of cohort studies. Eur J Prev Cardiol. 2019;26:145\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/2047487318780466\u003c/span\u003e\u003cspan address=\"10.1177/2047487318780466\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavn Jacobsen M, Jabbari R, Glinge C, Kj\u0026aelig;r Stampe N, Butt JH, Blanche P, et al. Potassium disturbances and risk of ventricular fibrillation among patients with ST-segment\u0026ndash;elevation myocardial infarction. J Am Heart Assoc. 2020;9:e014160. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/JAHA.119.014160\u003c/span\u003e\u003cspan address=\"10.1161/JAHA.119.014160\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLawton JS, Tamis-Holland JE, Bangalore S, Bates ER, Beckie TM, Bischoff JM, et al. 2021 ACC/AHA/SCAI guideline for coronary artery revascularization: A report of the American college of cardiology/american heart association joint committee on clinical practice guidelines. Circulation. 2022;145. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIR.0000000000001038\u003c/span\u003e\u003cspan address=\"10.1161/CIR.0000000000001038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThygesen K, Alpert JS, Jaffe AS, Chaitman BR, Bax JJ, Morrow DA et al. Fourth Universal Definition of Myocardial Infarction (2018). Circulation 2018;138. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIR.0000000000000617\u003c/span\u003e\u003cspan address=\"10.1161/CIR.0000000000000617\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKardalas E, Paschou SA, Anagnostis P, Muscogiuri G, Siasos G, Vryonidou A. Hypokalemia: A clinical update. Endocr Connect. 2018;7:R135\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1530/EC-18-0109\u003c/span\u003e\u003cspan address=\"10.1530/EC-18-0109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJensen CJ, Nielsen JK, Talbott MM, O\u0026rsquo;Connell D, Patel VS, Armstrong PA, et al. Effects of serum potassium on mortality in patients with ST-elevation myocardial infarction. Cureus. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7759/cureus.61126\u003c/span\u003e\u003cspan address=\"10.7759/cureus.61126\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUluganyan M, Ekmekci A, Murat A, Avsar S, Ulutas TK, Uyarel H, et al. Admission serum potassium level is associated with in-hospital and long-term mortality in ST-elevation myocardial infarction. Anatol J Cardiol. 2015. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5152/akd.2015.5706\u003c/span\u003e\u003cspan address=\"10.5152/akd.2015.5706\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSato N, Minami Y, Ako J, Maeda A, Akashi Y, Ikari Y, et al. Clinical significance of prehospital 12-lead electrocardiography in patients with ST-segment elevation myocardial infarction presenting with syncope: From a multicenter observational registry (K-ACTIVE study). Heart Vessels. 2021;36:1466\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00380-021-01832-z\u003c/span\u003e\u003cspan address=\"10.1007/s00380-021-01832-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTazmini K, Frisk M, Lewalle A, Laasmaa M, Morotti S, Lipsett DB, et al. Hypokalemia promotes arrhythmia by distinct mechanisms in atrial and ventricular myocytes. Circ Res. 2020;126:889\u0026ndash;906. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIRCRESAHA.119.315641\u003c/span\u003e\u003cspan address=\"10.1161/CIRCRESAHA.119.315641\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBikou O, Kho C, Ishikawa K. Atrial stretch and arrhythmia after myocardial infarction. Aging. 2018;11:11\u0026ndash;2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18632/aging.101729\u003c/span\u003e\u003cspan address=\"10.18632/aging.101729\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Y, Kong X-J, Ji Y-Y, Fan J, Ji C-C, Chen X-M, et al. Serum electrolyte concentrations and risk of atrial fibrillation: An observational and mendelian randomization study. BMC Genomics. 2024;25:280. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12864-024-10197-2\u003c/span\u003e\u003cspan address=\"10.1186/s12864-024-10197-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Han D, Li G. Electrocardiographic manifestations in severe hypokalemia. J Int Med Res. 2020;48:0300060518811058. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0300060518811058\u003c/span\u003e\u003cspan address=\"10.1177/0300060518811058\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZareei M, Zareiamand H, Kamali M, Ardalani N, Ebrahimi A, Nabati M. Can prolonged P-R interval predict clinical outcomes in non-ST elevation acute coronary syndrome patients? BMC Cardiovasc Disord. 2024;24:137. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12872-024-03809-y\u003c/span\u003e\u003cspan address=\"10.1186/s12872-024-03809-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKihlgren M, Almqvist C, Amankhani F, Jonasson L, Norman C, Perez M, et al. The U-wave: A remaining enigma of the electrocardiogram. J Electrocardiol. 2023;79:13\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jelectrocard.2023.03.001\u003c/span\u003e\u003cspan address=\"10.1016/j.jelectrocard.2023.03.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamadurai S, Varadarajan V, Harini SS, T T, Varadarajan VK. Electrocardiographic changes in patients with hypokalemia and their correlation with serum potassium levels. Cureus. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7759/cureus.91922\u003c/span\u003e\u003cspan address=\"10.7759/cureus.91922\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMp G, Gupta MD, Mukhopadhyay S, Yusuf J, Tn SR. U wave: An important noninvasive electrocardiographic diagnostic marker. Indian Pacing Electrophysiol J 2005.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Y, Chen Y, Liang S, Li Y, Zhao C, Wu Z. Association between potassium fluctuation and in-hospital mortality in acute myocardial infarction patients: a retrospective analysis of the MIMIC‐IV database. Clin Res Cardiol. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00392-025-02613-8\u003c/span\u003e\u003cspan address=\"10.1007/s00392-025-02613-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFax\u0026eacute;n J, Xu H, Evans M, Jernberg T, Szummer K, Carrero J-J. Potassium levels and risk of in-hospital arrhythmias and mortality in patients admitted with suspected acute coronary syndrome. Int J Cardiol. 2019;274:52\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijcard.2018.09.099\u003c/span\u003e\u003cspan address=\"10.1016/j.ijcard.2018.09.099\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThongprayoon C, Cheungpasitporn W, Thirunavukkarasu S, Petnak T, Chewcharat A, Bathini T, et al. Serum Potassium Levels at Hospital Discharge and One-Year Mortality among Hospitalized Patients. Med (Mex). 2020;56:236. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/medicina56050236\u003c/span\u003e\u003cspan address=\"10.3390/medicina56050236\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong T, Hui W, Huang M, Guo Y, Yu M, Yang X, et al. Dynamic changes in ion channels during myocardial infarction and therapeutic challenges. Int J Mol Sci. 2024;25:6467. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms25126467\u003c/span\u003e\u003cspan address=\"10.3390/ijms25126467\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ST-segment elevation myocardial infarction, pre-hospital assessment, prediction model, nomogram, hypokalemia","lastPublishedDoi":"10.21203/rs.3.rs-8185679/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8185679/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHypokalemia is common in patients with ST-segment elevation myocardial infarction (STEMI) and significantly elevates the risk of life-threatening arrhythmias and mortality. Yet no validated prehospital prediction tool exists to identify this high-risk condition early.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo develop and validate a prehospital prediction model for hypokalemia in STEMI patients using readily available clinical and electrocardiographic parameters.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective observational study was conducted involving 320 STEMI patients admitted to the Second Affiliated Hospital of Soochow University between January 2023 and December 2024. Patients were categorized into hypokalemia (n\u0026thinsp;=\u0026thinsp;114) and non-hypokalemia (n\u0026thinsp;=\u0026thinsp;206) groups based on initial serum potassium levels. Univariate logistic regression, least absolute shrinkage and selection operator(LASSO), and multivariate logistic regression were used to identify independent predictors. A nomogram was constructed and evaluated for discrimination, calibration, and clinical utility.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFive independent predictors were identified: symptom-to-door time (OR\u0026thinsp;=\u0026thinsp;0.85, 95% CI: 0.78\u0026ndash;0.94), syncope/coma (OR\u0026thinsp;=\u0026thinsp;3.57, 95% CI: 1.12\u0026ndash;11.37), atrial arrhythmia (OR\u0026thinsp;=\u0026thinsp;4.18, 95% CI: 1.33\u0026ndash;13.17), PR interval (OR\u0026thinsp;=\u0026thinsp;1.01, 95% CI: 1.00\u0026ndash;1.02), and U wave (OR\u0026thinsp;=\u0026thinsp;5.20, 95% CI: 2.59\u0026ndash;10.46). The prediction model demonstrated good discrimination with an AUC of 0.735 (95% CI: 0.680\u0026ndash;0.791). Calibration curves and decision curve analysis confirmed satisfactory model performance and clinical usefulness.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe developed a practical and validated nomogram for predicting prehospital hypokalemia in STEMI patients using five easily obtainable clinical and ECG variables. This tool may facilitate early identification and intervention in high-risk individuals, potentially improving prehospital management and clinical outcomes.\u003c/p\u003e","manuscriptTitle":"Prehospital Prediction of Hypokalemia in patients with ST‑Segment Elevation Myocardial Infarction: Development and Validation of a Prediction Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-31 10:43:45","doi":"10.21203/rs.3.rs-8185679/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"42cff7f0-4275-4081-8451-9ab156db18ea","owner":[],"postedDate":"December 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T08:28:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-31 10:43:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8185679","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8185679","identity":"rs-8185679","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}