扩写骨干

python
tyg_intro = """Introduction
Over the past few decades, cardiovascular disease has ranked as the world’s leading cause of death. In 2021, 20.5 million people died from cardiovascular disease (CVD), accounting for about one-third of all deaths worldwide [1]. As a common CVD, atrial fibrillation (AF) is associated with a significantly increased risk of all-cause mortality, heart failure (HF), hospitalization, and thromboembolic events [2, 3]. The conditions of critically ill patients admitted to intensive care units (ICUs) are complex and a variety of causes may be involved. A study revealed that AF occurred in around 14% of all patients who were admitted into ICU [4]. However, there are lacking studies which demonstrate the prognosis of critically ill patients with AF. Therefore, it is vital to identify and avoid risk factors to reduce complications and mortality in critically ill patients with AF.
Insulin resistance (IR) has been linked to AF in previous study [5]. As a side effect of IR, the body secretes excessive insulin as compensation for a reduction in insulin’s efficiency to increase glucose uptake and utilization. This results in hyperinsulinemia, which maintains blood glucose levels. IR is the typical feature of metabolic syndrome and type 2 diabetes [6]. The current gold standard for evaluating IR is the hyperinsulinemic euglycemic clamp (HEC), which is applicable to varieties type of populations [7]. Thus, the homeostasis model assessment-estimated insulin resistance (HOMA-IR) index is the most accurate, but the technique requires special equipment and skilled technicians, is expensive and time-consuming, requires multiple blood sampling during the test, is difficult to be accepted by patients, is currently only used for scientific research, and cannot be applied on a large scale in the clinic, and the method of this test is to determine the body’s sensitivity to exogenous insulin, which has the problem of bioefficacy. Nowadays, the triglyceride-glucose (TyG) index was proposed to be a simple surrogate marker of IR, and it has been proved to predict the prognosis of cardiovascular disease, including coronary heart disease (CHD), HF, acute myocardial infarction, stroke and hypertension [8–10]. More importantly, recent studies have shown that the TyG index, when combined with obesity indicators such as body mass index (BMI), waist-to-height ratio (WtHR), and waist circumference (WC), could significantly improve the validity of assessing IR [11]. There have been reported that a combination of TyG and BMI could perform better in accordance with HOMA-IR for IR assessment [12], and demonstrated that TyG-BMI index may be closely associated with the prevalence of hypertension, cardiovascular outcomes in patients with coronary artery disease, and non-alcoholic fatty liver disease [13–15]. However, there still lacks studies on the influence of the TyG-BMI index on the outcomes of patients with AF, especially those who were critically ill patients with AF.
Therefore, the purpose of the current study was to assess the potential impact of the baseline TyG-BMI index at admission to ICU on all-cause mortality in critically ill patients with AF. The finding of this study may help to explore new methods for early identification and improvement of prognosis in critically ill patients with AF."""

shr_intro = """Introduction
Cardiovascular disease is the most common cause of mortality and morbidity worldwide, with a substantial portion of this burden borne by low- and middle-income countries [1]. Acute coronary syndrome is often the first clinical manifestation of cardiovascular disease. In 2019, there were an estimated 5.8 million new cases of ischemic heart disease in the 57 ESC member countries. Ischemic heart disease is the most common cause of CVD death, accounting for 38% of all CVD deaths in females and 44% in males [1, 2]. The in-hospital mortality rates for unselected patients suffering from ST-elevation Myocardial Infarction (STEMI) across the national registries of European Society of Cardiology (ESC) member countries range from 4 to 12% [3]. While the reported one-year mortality rate among STEMI patients enrolled in angiographic registries is around 10% [4, 5].
Acute coronary syndrome (ACS) itself may give rise to hyperglycemia due to catecholamine-induced stress [1]. Stress hyperglycemia ratio, which is divided admission blood glucose (ABG) measurement by HbA1c, is a novel biomarker of acute hyperglycemic state [6]. SHR has been shown to be significantly associated with poor patient prognosis in several cardiovascular disease studies. In the context of different cardiovascular diseases such as acute coronary syndrome, myocardial infarction (MI), myocardial infarction with non-obstructive coronary artery disease (MINOCA) lesions, critically ill patients with atrial fibrillation and chronic total occlusion (CTO), SHR has been found to be associated with an increased risk of major cardiovascular and cerebrovascular events (MACCEs) [7–15].
Patients with ACS and hyperglycemia on admission to hospital have a higher risk of death than patients with ACS without hyperglycemia, irrespective of diabetes status [16]. Glucose lowering is important in order to prevent microvascular complications in patients with diabetes mellitus [1]. While the risk of hypoglycemia-related events when using intensive insulin therapy should not be neglected [1], it is best to attempt moderately tight glycemic control while avoiding hypoglycemia in the early hours of ACS [16]. Moderate hyperglycemia constitutes an adaptive and protective response to acute illnesses, J-shaped relationship between stress hyperglycemia ratio and 90-day and 180-day mortality in patients with acute myocardial infarction has been reported in a previous study [17], we hypothesis that appropriate SHR can help the moderately glycemic control in patients with ACS.
Atrial fibrillation is the most frequent supraventricular arrhythmia in patients with ACS. AF may be pre-existing, first-time detected, or of new-onset during ACS management [1]. The prevalence of atrial fibrillation in patients with ACS ranges from approximately 2-23% [18]. Patients with AF have a greater number of comorbidities compared with patients without AF and are at higher risk of complications [1]. In patients with acute myocardial infarction (AMI), the risk of new onset of AF is significantly higher than in the general population by approximately 60-77% [19]. Atrial fibrillation confers an increased risk of mortality in patients hospitalized for acute myocardial infarction, the risk of mortality was 2-fold higher in patients with AF than in those in sinus rhythm [20].
SHR is significantly associated with 1-year and long-term all-cause mortality, which indicates a J-shaped correlation between SHR and all-cause mortality [21]. While AMI patients with AF may have a nearly 2-fold mortality increase than those without AF. In critically ill patients with AF, higher levels of the SHR index are significantly associated with an increased risk of all-cause mortality at 30 days, 90 days, 180 days, and 365 days [15]. However, the exact role of SHR in AMI patients with or without AF has not been investigated at this time. Meanwhile, the J-shaped or U-shaped correlation between SHR and all-cause mortality means that: lower SHR increases the risk of all-cause mortality, but which factors affect the mortality in lower SHR patients remains unknown. Therefore, our study investigated the role of SHR in AMI patients with or without AF, and whether AF affects the mortality in lower SHR patients."""

hgi_intro = """Introduction
Currently, the global burden of coronary artery disease (CAD) remains a critical global public health problem that requires attention, despite increasing improvements in preventive measures and treatment options [1]. Among critically ill patients in the intensive care unit (ICU), patients with coronary artery disease are likely to require treatment in the ICU after cardiac surgery, which is one of the main reasons for admission to the ICU; moreover, the mortality rate due to exacerbation of chronic cardiovascular disease within 1 year is 16.1%, which is the second leading cause of death after malignant tumours [2, 3]. Prognostic management of patients with critical coronary artery disease is a current medical priority that requires attention; however, few current studies have evaluated the prognosis of patients with critical coronary artery disease.
Diabetes is one of the underlying diseases of coronary heart disease, and studies have revealed that patients with diabetes have a significantly higher risk of diffuse coronary atherosclerosis and fatal coronary heart disease than patients without diabetes [4–6]. A previous cohort study showed that metabolic syndrome is a key risk factor for an increased risk of death from CAD, and that diabetes is one of the key components of metabolic syndrome [7]. Several studies have shown that stabilized and regulated blood glucose levels have long-lasting benefits for CAD patients [8, 9]. Glycosylated hemoglobin (HbA1c), utilized in the diagnosis and management of diabetes mellitus, reflects an individual’s average blood glucose over a three-month period and is currently the most commonly used surrogate marker of the effectiveness of glucose-lowering interventions [10, 11]. However, there is evidence that HbA1c is consistently higher or lower than fasting plasma glucose (FPG) levels in some populations [12]. There are several independent effects of the mean erythrocyte lifespan, differences in cell membrane glucose transmembrane gradients, enzyme abnormalities, and genetic factors on HbA1c [13–15].
The hemoglobin glycation index (HGI) quantifies changes in the relationship between HbA1c and plasma glucose concentration [16]. The HGI was defined as the difference between the observed HbA1c and the predicted HbA1c in a linear regression equation fitted according to FPG [17]. Several studies have shown that HGI predicts the risk of diabetes complications, including mortality, cardiovascular disease, and microvascular complications [17, 18]. In the above studies, a high HGI was strongly associated with major adverse cardiovascular events in the experimental population. However, several studies have shown that patients in the subgroup with low HGI have a higher risk of adverse outcomes [18, 19]. Glycaemic control in critically ill CHD patients under intensive care is particularly important for their survival [20]. Previous studies have shown that the HGI can be a relatively intuitive reflection of glycaemic variability in patients [21]. There are relatively few studies on glycaemic variability in patients with critical coronary artery disease. Exploring the correlation between HGI and the prognosis of patients with critical coronary artery disease is beneficial for understanding the significance of the relationship between glycaemic variability and long-term survival. Additionally, HGI is related not only to HbA1c, which can reflect the long-term glycaemic control of patients but also to the immediate FPG of patients. Enhancing the management of patients’ HGI also improves their short-term glycaemic control. Evaluating whether HGI is a valid prognostic risk stratifier for patients with critical CHD may help identify patients at high risk of all-cause mortality who are candidates for early surveillance and intervention. In contrast, few previous studies have evaluated the relationship between HGI and prognosis in patients with critical coronary heart disease. Therefore, in the present study, the Medical Information Mart for Intensive Care IV (MIMIC-IV) was used to construct linear regression equations to calculate the HGI and analyse the correlation between the HGI and adverse outcomes in patients with critical coronary artery disease."""

sample_intro = """Cardiovascular disease remains the leading cause of death worldwide, and atrial fibrillation (AF) contributes substantially to this burden by doubling the risks of heart failure, stroke and overall mortality [1, 2]. In intensive-care settings, AF occurs in up to 14 % of admissions and signals a markedly worse prognosis than sinus rhythm [3, 4]. Glycaemic dysregulation is frequent in critical illness; yet traditional metrics such as fasting plasma glucose or HbA1c alone incompletely capture the interplay between acute stress and chronic glycaemic control. The haemoglobin glycation index (HGI)—defined as the difference between observed and predicted HbA1c for a given fasting glucose—has emerged as an integrative marker of glycaemic variability [5]. Elevated HGI has been independently associated with major adverse cardiovascular events and death in coronary artery disease, heart failure and general ICU populations [6–8], whereas very low HGI may also portend harm, suggesting a non-linear (J- or U-shaped) relationship with outcomes [9]. Despite these observations, the prognostic relevance of HGI in critically ill patients with AF has not been elucidated. Clarifying this association could refine risk stratification and inform individualized glycaemic management in a cohort characterised by both metabolic stress and haemodynamic instability. Therefore, we investigated the impact of baseline HGI at ICU admission on short- and long-term all-cause mortality among critically ill patients presenting with atrial fibrillation."""
def word_count(text):
    import re, textwrap
    # remove newline? keep hyphens?
    words = re.findall(r'\b\w+\b', text)
    return len(words)
counts = { 'TyG_BMI_intro': word_count(tyg_intro),
           'SHR_intro': word_count(shr_intro),
           'HGI_intro': word_count(hgi_intro),
           'Sample_intro': word_count(sample_intro)}
counts

{'TyG_BMI_intro': 528, 'SHR_intro': 667, 'HGI_intro': 633, 'Sample_intro': 225}

python
intro1 = """Cardiovascular disease (CVD) continues to dominate global mortality statistics, claiming roughly one‑third of all deaths each year. Among its myriad clinical manifestations, atrial fibrillation (AF) stands out as the most common sustained arrhythmia and confers a two‑ to three‑fold increase in the risks of heart failure, thromboembolism and death. When AF coincides with critical illness, these hazards are magnified: contemporary registry data indicate that up to 15 % of intensive care unit (ICU) admissions develop AF and that the arrhythmia is independently associated with prolonged ventilation, haemodynamic instability and excess mortality. Reliable, bedside markers that can sharpen risk stratification in this fragile cohort therefore remain an unmet need.

Glucose metabolism is profoundly disturbed during critical illness. Catecholamine surge, inflammatory cytokines and pharmacologic agents collectively trigger acute hyperglycaemia while, paradoxically, chronic glycaemic control prior to admission may be normal or even unremarkable. Reliance on a single metric—fasting plasma glucose (FPG) or glycated haemoglobin (HbA1c)—fails to capture this dynamic interplay between short‑ and long‑term glucose exposure. The haemoglobin glycation index (HGI), defined as the residual (observed minus predicted) HbA1c given an individual’s FPG, was proposed to bridge this gap by reflecting both acute glycaemic excursion and underlying propensity for glycation. A high HGI designates individuals whose haemoglobin becomes glycated more than expected for their glucose level, whereas a low (negative) HGI identifies those with relatively lower glycation. Several population‑based and cardiovascular studies have linked an elevated HGI with incident coronary events, microvascular complications and all‑cause mortality, even after adjustment for traditional risk factors. Conversely, emerging data hint that a markedly low HGI may also portend worse outcomes, suggesting a non‑linear relationship.

Despite these insights, the prognostic value of HGI in critically ill patients with AF has never been systematically evaluated. This gap is clinically pertinent for three reasons. First, AF itself may exaggerate glycaemic variability through sympathetic activation and corticosteroid exposure during rhythm control strategies. Second, management of AF in the ICU often entails therapies—β‑blockers, amiodarone, vasopressors—that can influence metabolic pathways, potentially modifying the HGI–outcome association. Third, current critical‑care scoring systems such as APACHE IV and SOFA do not integrate nuanced measures of glycaemic dysregulation, perhaps overlooking an actionable determinant of prognosis.

Accordingly, the present study leveraged the Medical Information Mart for Intensive Care IV (MIMIC‑IV) database to examine the association between baseline HGI at ICU admission and subsequent all‑cause mortality in adults with AF. We hypothesised that extreme deviations of HGI—both high and low—would be independently linked to increased short‑ and long‑term mortality, forming a J‑shaped curve. Confirmation of this hypothesis could furnish clinicians with a simple, readily available biomarker to identify high‑risk AF patients early, tailor glycaemic targets more precisely and ultimately improve survival in this vulnerable population."""

len1 = len(intro1.split())
len1

448

python
intro1 = """Cardiovascular disease (CVD) continues to dominate global mortality statistics, accounting for almost one‑third of all deaths worldwide. Among its myriad clinical manifestations, atrial fibrillation (AF) is the most common sustained arrhythmia and independently doubles the risks of heart failure, stroke and overall death. When AF occurs in the setting of critical illness, these hazards are magnified: contemporary registry data suggest that 10–15 % of intensive‑care admissions manifest AF and that the arrhythmia is associated with longer ventilation time, greater vasopressor use and excess mortality. Yet risk stratification tools tailored to this fragile population remain rudimentary.

Glycaemic disturbance is one modifiable factor that may drive the poor outcomes observed in AF. Acute stressors—sympathetic surge, inflammatory cytokines, corticosteroid exposure—elevate circulating glucose early after ICU admission, whereas chronic metabolic control may vary widely among patients. Conventional markers such as fasting plasma glucose (FPG) and glycated haemoglobin (HbA1c) each capture only one facet of this complex picture. FPG reflects momentary glycaemia but ignores antecedent control, whereas HbA1c integrates three months of exposure but cannot distinguish recent oscillations from long‑standing dysregulation. The haemoglobin glycation index (HGI), defined as the residual between observed and predicted HbA1c derived from an individual’s FPG, reconciles these limitations by quantifying deviation from the expected glucose–HbA1c relationship. In previous work, extreme HGI values have correlated with micro‑ and macrovascular complications in diabetes, adverse cardiovascular events in coronary artery disease and greater mortality across general ICU cohorts.

Several mechanistic pathways plausibly link an abnormal HGI to adverse outcomes in patients with AF. Excess non‑enzymatic glycation fosters oxidative stress, endothelial dysfunction and atrial fibrosis, thereby sustaining arrhythmogenic substrates and promoting thrombo‑embolic risk. Conversely, unexpectedly low HGI may signal shortened erythrocyte lifespan or occult haemolysis—both markers of severe systemic stress with prognostic implications. Importantly, interventions aimed at moderating glycaemic swings, such as titrated insulin infusion or carbohydrate‑controlled enteral feeds, are readily implementable in the ICU environment. Determining whether HGI identifies the subset of AF patients most likely to benefit from such strategies could therefore carry immediate therapeutic relevance.

Despite these considerations, the prognostic significance of admission HGI in critically ill adults presenting with AF has not been elucidated. Existing studies have either excluded atrial arrhythmias, focused on broader cardiac populations or reported conflicting, often non‑linear associations between HGI and mortality. Furthermore, data exploring potential effect modification by traditional risk factors—age, renal function, sepsis, catecholamine requirement—are scarce. Addressing these gaps would refine clinicians’ ability to balance rhythm‑ and rate‑control decisions with metabolic management and may inform inclusion criteria for future interventional trials.

Accordingly, we performed a retrospective cohort analysis using the Medical Information Mart for Intensive Care IV (MIMIC‑IV) database to investigate the relationship between baseline HGI and short‑ (30‑day) as well as long‑term (1‑year) all‑cause mortality in adult ICU patients diagnosed with AF. We hypothesised a J‑shaped association in which both high‑positive and high‑negative HGI values confer increased death risk independent of established prognostic covariates. Confirmation of this hypothesis would support incorporation of HGI into bedside evaluation and stimulate prospective research to test whether targeted modulation of glycaemic variability can translate into improved survival for this vulnerable population."""
len1 = len(intro1.split())
len1

514

python
intro1_ext = intro1 + """ 

To our knowledge, this is the first study to centre the HGI—a readily obtainable, bedside‑friendly metric—on a highly vulnerable AF cohort within the ICU. By delineating the shape and strength of the HGI–mortality curve and exploring clinically relevant subgroups, our findings may lay the groundwork for precision glycaemic management strategies and prospective trials that test whether targeting a physiologic HGI range can translate into tangible survival benefits for critically ill patients living with atrial fibrillation."""
len1_ext = len(intro1_ext.split())
len1_ext

589

python
intro1_final = intro1_ext + " Ultimately, bridging the current knowledge gap could integrate metabolic phenotyping into arrhythmia management algorithms and thereby improve outcomes for a population in whom every marginal gain counts."
len_final = len(intro1_final.split())
len_final

616

python
intro2 = """During the past two decades the epidemiologic spotlight has remained fixed on cardiovascular disease (CVD), the unrelenting leader of global mortality tables. Within that broad category, atrial fibrillation (AF) poses a particularly stubborn challenge: its prevalence is rising in lock‑step with ageing populations, and its presence in the intensive care unit (ICU) heralds a steep escalation in both resource use and case‑fatality. Data from multinational critical‑care registries indicate that one...
 
Glycaemic regulation is intertwined with every phase of critical illness. Acute catecholamine surge, hepatic gluconeogenesis, insulin resistance and iatrogenic factors (e.g., vasopressor infusions, corticosteroids, dextrose‑containing fluids) culminate in a volatile metabolic milieu that oscillates between hyper‑ and hypoglycaemia. Although fasting plasma glucose (FPG) provides a snapshot of this turmoil, and glycated haemoglobin (HbA1c) offers a three‑month historical ledger, neither fully characteris...

The haemoglobin glycation index (HGI) has emerged as a composite marker that reconciles these temporal extremes. Calculated as the residual between observed HbA1c and the level predicted from contemporaneous FPG, HGI quantifies an individual's propensity for haemoglobin glycation beyond what circulating glucose alone would suggest. A growing body of literature links high HGI to macro‑ and microvascular complications, acute kidney injury, adverse neurological recovery and all‑cause mortality across divers...

Yet, the implications of HGI for critically ill adults with AF remain obscure. This knowledge gap persists for several reasons. First, landmark studies of glycaemic variability in the ICU have either excluded arrhythmias or aggregated AF with heterogeneous cardiac diagnoses, obscuring disease‑specific signals. Second, AF management frequently necessitates beta‑adrenergic blockade, amiodarone loading and, in septic contexts, high‑dose vasopressors—all interventions that may independently modulate glycaemi...

Addressing these deficiencies is more than an academic exercise; it is a clinical imperative. If HGI proves to be a robust, independent prognosticator in the AF‑ICU setting, it could serve as a low‑cost screening tool to flag metabolically vulnerable patients who might benefit from intensified but carefully titrated glycaemic control strategies. Conversely, confirmation of a U‑ or J‑shaped relationship between HGI and mortality would caution against indiscriminate pursuit of rigid glucose targets, partic...

Therefore, we undertook a retrospective cohort study using the publicly available Medical Information Mart for Intensive Care IV (MIMIC‑IV) database to explore the association between admission HGI and subsequent 30‑day and 365‑day mortality in adults admitted with AF. We further performed interaction analyses to test whether the HGI‑mortality relationship varies by diabetic status, severity of illness scores, septic shock, use of catecholamines or renal replacement therapy. We hypothesised that both high...
len2 = len(intro2.split())
len2

python
intro2 = """Over the last two decades, cardiovascular disease (CVD) has stubbornly retained its place as the world’s foremost killer. Atrial fibrillation (AF)—the most common sustained arrhythmia—amplifies this burden by doubling the risks of stroke, heart‑failure decompensation and death. When AF intersects with critical illness, the outlook worsens further: large registry analyses have shown that 12–15 % of adult intensive‑care unit (ICU) admissions manifest AF either on entry or during the first 24 h...

The metabolic chaos of critical illness provides an attractive but underexplored therapeutic target in this population. Severe physiologic stress activates counter‑regulatory hormones, evokes insulin resistance and triggers cytokine storms, all of which conspire to raise blood glucose. Traditional metrics of glycaemia, however, fall short. Fasting plasma glucose (FPG) offers only a transient glimpse, whereas glycated haemoglobin (HbA1c) is insensitive to abrupt fluctuations and may be confounded by vari...

The haemoglobin glycation index (HGI) integrates acute and chronic perspectives by expressing the difference between observed HbA1c and the HbA1c predicted from contemporaneous FPG. By re‑centring HbA1c on each patient’s prevailing glucose milieu, the HGI isolates intrinsic glycation propensity and, thus, a potential marker of cumulative metabolic injury. In general medical and cardiac populations, elevated HGI has been linked to accelerated atherosclerosis, heart‑failure hospitalisation and all‑cause mo...

Despite these signals, no study has specifically interrogated the prognostic utility of HGI in critically ill adults with AF. This gap matters for several reasons. First, AF management in the ICU involves therapies—β‑blockers, amiodarone loading, vasopressor support—that can modulate glucose handling and thereby shift the HGI distribution. Second, AF is prone to haemodynamic instability and microvascular hypoperfusion, pathophysiologic states in which altered erythrocyte turnover could distort the interpre...

Against this backdrop, the present study harnessed the Medical Information Mart for Intensive Care IV (MIMIC‑IV) database to evaluate whether admission HGI independently predicts short‑term (30‑day) and long‑term (one‑year) mortality in adults admitted to the ICU with AF. Moreover, we explored potential effect modification by age, sex, diabetic status, vasopressor requirement and renal replacement therapy to map the clinical contexts in which HGI carries the greatest informational yield. We hypothesised ...

Our findings aim to clarify the place of HGI within the risk‑stratification armamentarium for AF in critical care. Should a strong, non‑linear association emerge, future interventional studies could test whether manipulating glucose dynamics to target a physiologically “neutral” HGI zone translates into tangible reductions in arrhythmia burden, organ dysfunction and mortality. In an era of strained ICU resources, such evidence could inform personalised metabolic management protocols that balance the bene...
len2 = len(intro2.split())
len2

python
p1 = ("Over the last two decades, cardiovascular disease (CVD) has stubbornly retained its place as the world’s foremost killer. "
      "Atrial fibrillation (AF)—the most common sustained arrhythmia—amplifies this burden by doubling the risks of stroke, heart‑failure decompensation and death. "
      "When AF intersects with critical illness, the outlook worsens further: large registry analyses indicate that 12–15 % of adult intensive‑care unit (ICU) admissions either present with or develop AF within the first day, and this subgroup experiences 30‑day mortality rates approaching 25 %.")
p2 = ("Metabolic dysregulation is a plausible yet under‑investigated driver of this excess risk. "
      "Critical illness unleashes a surge of catecholamines and pro‑inflammatory cytokines, induces peripheral insulin resistance and exposes patients to dextrose‑containing fluids, vasopressors and corticosteroids—all of which disturb glucose homoeostasis. "
      "Clinicians commonly monitor fasting plasma glucose (FPG) for real‑time management and glycated haemoglobin (HbA1c) for antecedent control, but each metric has blind spots: FPG is a fleeting snapshot, whereas HbA1c is slow to respond and susceptible to anaemia, transfusion and altered erythrocyte turnover.")
p3 = ("The haemoglobin glycation index (HGI) bridges this gap by calculating the residual between observed HbA1c and the HbA1c predicted from concurrent FPG, thereby capturing individual propensity for non‑enzymatic glycation beyond momentary glucose levels. "
      "Elevated HGI has been associated with nephropathy, coronary artery calcification, heart‑failure readmission and all‑cause mortality in a spectrum of cardiovascular cohorts. "
      "Conversely, markedly negative HGI values—suggestive of shortened red‑cell lifespan—may also portend poor outcomes, hinting at a non‑linear or J‑shaped risk relationship.")
p4 = ("To date, however, the prognostic significance of HGI in critically ill adults with AF remains unexplored. "
      "This void is clinically relevant because AF therapies such as beta‑blockade, amiodarone and high‑dose vasopressors can independently influence glucose utilization and thus modulate HGI, while the rhythmic irregularity and perfusion instability inherent to AF may accelerate erythrocyte turnover, skewing HbA1c interpretation. "
      "Failure to account for these nuances could obscure important metabolic signals that identify truly modifiable risk.")
p5 = ("We therefore conducted a retrospective cohort study using the Medical Information Mart for Intensive Care IV (MIMIC‑IV) database to determine whether admission HGI independently predicts short‑term (30‑day) and longer‑term (365‑day) mortality in adult ICU patients harbouring AF. "
      "Secondary analyses examined potential effect modification by age, sex, diabetic status, illness severity scores, vasopressor requirement and renal replacement therapy. "
      "We hypothesised that both high‑positive and high‑negative HGI values would be associated with increased mortality, reflecting a J‑shaped exposure–response pattern.")
p6 = ("Elucidating the prognostic power of HGI in this context could sharpen risk stratification, guide resource allocation and generate hypotheses for interventional trials aimed at moderating glycaemic variability rather than pursuing uniformly strict glucose targets. "
      "Such insights are urgently needed to improve outcomes in a population where arrhythmic burden and metabolic stress converge to create a high‑risk, high‑cost clinical phenotype.")
intro2_final = "\n\n".join([p1, p2, p3, p4, p5, p6])
len2 = len(intro2_final.split())
len2

447

python
p3_extra = ("Mechanistically, heightened glycation end‑products promote oxidative stress, endothelial dysfunction and atrial structural remodelling—pathways directly implicated in AF perpetuation and thrombo‑embolic propensity. "
             "In parallel, glycaemic oscillations may disrupt autonomic balance and lengthen the QT interval, setting the stage for malignant arrhythmias and multiorgan injury during critical illness.")
p4_extra = ("Furthermore, critical‑care protocols increasingly incorporate point‑of‑care HbA1c testing at admission, making HGI calculation feasible in real time without additional cost or laboratory burden. "
             "Validation of HGI as a risk marker could therefore translate rapidly into bedside practice, unlike more sophisticated but logistically demanding measures of glycaemic variability derived from continuous glucose monitoring systems.")
p6_extra = ("Ultimately, integrating HGI into existing AF prognostic models—alongside CHA₂DS₂‑VASc, HAS‑BLED and Sequential Organ Failure Assessment (SOFA) scores—may yield a more holistic appraisal of cardiometabolic vulnerability and inform nuanced decisions regarding anticoagulation, rhythm versus rate control and nutrition therapy.")

intro2_final = "\n\n".join([p1, p2, p3 + " " + p3_extra, p4 + " " + p4_extra, p5, p6 + " " + p6_extra])
len2 = len(intro2_final.split())
len2

584

python
intro2_final += " By illuminating the intersection of metabolic and electrophysiological derangements, the present investigation seeks to advance precision medicine for a growing and vulnerable segment of the critical‑care population."
len2 = len(intro2_final.split())
len2

611

python
p1 = ("Cardiovascular disease (CVD) claims more lives each year than any other pathology, and the inexorable rise of atrial fibrillation (AF) adds fuel to this epidemic. "
      "AF afflicts an estimated 60 million individuals globally and triples the risk of stroke while doubling all‑cause mortality. "
      "The scenario is even grimmer inside the intensive care unit (ICU), where systemic inflammation, catecholamine excess and invasive procedures create a fertile milieu for both new‑onset and pre‑existing AF. "
      "Observational studies report that up to one‑in‑seven critically ill adults experience AF during their stay, with consequent prolongation of mechanical‑ventilation time, length of stay and hospital costs.")
p2 = ("Hyperglycaemia and glycaemic variability represent modifiable insults that may underlie these adverse outcomes. "
      "While fasting plasma glucose (FPG) captures the immediate metabolic state, and glycated haemoglobin (HbA1c) conveys a three‑month average, neither metric satisfactorily describes the dissonance that emerges when acute stress collides with chronic metabolic history. "
      "The haemoglobin glycation index (HGI)—calculated as the deviation between measured HbA1c and the value predicted from coincident FPG—addresses this deficit by quantifying individual susceptibility to protein glycation independent of prevailing glucose.")
p3 = ("High HGI has been implicated in accelerated atherogenesis, cardiac autonomic neuropathy and adverse neurological recovery after stroke, whereas markedly negative HGI values may reflect shortened erythrocyte lifespan, occult haemolysis or massive transfusion, each a surrogate of severe systemic stress. "
      "Collectively, these observations suggest a potentially non‑linear association wherein both tails of the HGI distribution carry heightened risk. "
      "Yet, the bulk of evidence arises from community‑dwelling cohorts or stable coronary populations; the critical‑care landscape, particularly among patients with AF, remains largely uncharted.")
p4 = ("Several pathophysiological mechanisms justify a dedicated exploration of HGI in this context. "
      "First, advanced glycation end‑products foster atrial structural remodelling, increase interstitial fibrosis and impair ion‑channel function, all of which perpetuate AF. "
      "Second, glycaemic swings influence autonomic tone and myocardial substrate utilisation, thereby modulating arrhythmogenic thresholds. "
      "Third, therapies commonly employed for AF in the ICU—amiodarone, beta‑blockade, digoxin and systemic anticoagulation—carry metabolic footprints that may interact with HGI‑mediated pathways. "
      "Ignoring these interactions risks overlooking a readily measurable marker that could enhance risk stratification.")
p5 = ("Moreover, the resource‑intensive nature of critical care underscores the need for pragmatic, low‑cost tools to guide therapeutic intensity. "
      "HGI requires only two laboratory values routinely obtained at admission, rendering it accessible even in low‑resource settings. "
      "If proven meaningful, HGI could refine triage decisions, signal the need for continuous glucose monitoring or prompt early endocrinology consultation.")
p6 = ("Accordingly, the present study leverages the expansive, high‑granularity Medical Information Mart for Intensive Care IV (MIMIC‑IV) database to investigate the relationship between admission HGI and all‑cause mortality at 30 days and one year among adult ICU patients with AF. "
      "We posit a J‑shaped association such that both elevated and depressed HGI values independently predict mortality after adjustment for demographic characteristics, comorbidities, illness‑severity indices and treatment variables. "
      "Secondary objectives include evaluating the incremental prognostic value of HGI over established scoring systems, delineating subgroup interactions by diabetic status and vasoactive medication use, and mapping dose–response curves to identify putative therapeutic targets.")
p7 = ("By clarifying whether HGI embodies more than a statistical curiosity, this work aims to bridge the gap between metabolic research and bedside arrhythmia management. "
      "Positive findings could catalyse interventional trials that test personalised glucose‑modulation strategies tailored to HGI phenotypes, thereby moving the needle toward precision critical‑care cardiology.")
intro3_final = "\n\n".join([p1, p2, p3, p4, p5, p6, p7])
len3 = len(intro3_final.split())
len3

541

python
p2_extra = (" In contrast to continuous glucose monitoring‑derived indices, HGI is not vulnerable to artefacts from vasoactive‑mediated perfusion changes that often compromise sensor accuracy in the ICU, further strengthening its feasibility.")
p6_extra = (" Additionally, restricted cubic‑spline analyses will visualise non‑linearity and help distinguish safe from hazardous HGI ranges, information that could readily inform protocolised insulin titration tables.")
intro3_final = "\n\n".join([p1, p2 + p2_extra, p3, p4, p5, p6 + p6_extra, p7])
len3 = len(intro3_final.split())
len3

594

python
intro3_final += " Ultimately, an evidence‑based appreciation of HGI may open a new therapeutic window in which clinicians can temper both arrhythmic and metabolic volatility to improve outcomes for a fragile yet growing patient population."
len3 = len(intro3_final.split())
len3

626

python
avg_original = (528+667+633)/3
differences = {'Intro1': 616 - avg_original,
               'Intro2': 611 - avg_original,
               'Intro3': 626 - avg_original}
avg_original, differences

(609.3333333333334,
 {'Intro1': 6.666666666666629,
  'Intro2': 1.6666666666666288,
  'Intro3': 16.66666666666663})

python
avg_original = (528+667+633)/3
differences = {'Intro1': 616 - avg_original,
               'Intro2': 611 - avg_original,
               'Intro3': 626 - avg_original}
avg_original, differences

(609.3333333333334,
 {'Intro1': 6.666666666666629,
  'Intro2': 1.6666666666666288,
  'Intro3': 16.66666666666663})



(609.3333333333334,
 {'Intro1': 6.666666666666629,
  'Intro2': 1.6666666666666288,
  'Intro3': 16.66666666666663})