Artificial intelligence (AI) is revolutionizing healthcare by providing tools for disease prediction, diagnosis, and patient management. This review focuses on two key AI methodologies in healthcare: predictive modeling and causal inference. Predictive models excel in identifying patterns to forecast outcomes but are limited in explaining the underlying causes. In contrast, causal inference focuses on understanding cause-and-effect relationships, which makes effective medical interventions possible. Although randomized controlled trials (RCTs) are the gold standard for causal inference, they face limitations including cost and ethical concerns. As alternatives, emulated RCTs and advanced machine learning techniques have emerged for estimating causal effects, bridging the gap between prediction and causality. Additionally, Shapley values and Local Interpretable Model-Agnostic Explanations improve the interpretability of complex AI models, making them more actionable in clinical settings. Integrating prediction and causal inference holds great promise for advancing personalized medicine, enhancing patient outcomes, and optimizing healthcare delivery. However, careful application of AI tools is crucial to avoid misinterpretation and maximize their potential.

Artificial intelligence (AI) is revolutionizing healthcare by providing tools for disease prediction, diagnosis, and patient management. This review focuses on two key AI methodologies in healthcare: predictive modeling and causal inference. Predictive models excel in identifying patterns to forecast outcomes but are limited in explaining the underlying causes. In contrast, causal inference focuses on understanding cause-and-effect relationships, which makes effective medical interventions possible. Although randomized controlled trials (RCTs) are the gold standard for causal inference, they face limitations including cost and ethical concerns. As alternatives, emulated RCTs and advanced machine learning techniques have emerged for estimating causal effects, bridging the gap between prediction and causality. Additionally, Shapley values and Local Interpretable Model-Agnostic Explanations improve the interpretability of complex AI models, making them more actionable in clinical settings. Integrating prediction and causal inference holds great promise for advancing personalized medicine, enhancing patient outcomes, and optimizing healthcare delivery. However, careful application of AI tools is crucial to avoid misinterpretation and maximize their potential.

Artificial intelligence (AI) is revolutionizing healthcare by providing tools for disease prediction, diagnosis, and patient management. This review focuses on two key AI methodologies in healthcare: predictive modeling and causal inference. Predictive models excel in identifying patterns to forecast outcomes but are limited in explaining the underlying causes. In contrast, causal inference focuses on understanding cause-and-effect relationships, which makes effective medical interventions possible. Although randomized controlled trials (RCTs) are the gold standard for causal inference, they face limitations including cost and ethical concerns. As alternatives, emulated RCTs and advanced machine learning techniques have emerged for estimating causal effects, bridging the gap between prediction and causality. Additionally, Shapley values and Local Interpretable Model-Agnostic Explanations improve the interpretability of complex AI models, making them more actionable in clinical settings. Integrating prediction and causal inference holds great promise for advancing personalized medicine, enhancing patient outcomes, and optimizing healthcare delivery. However, careful application of AI tools is crucial to avoid misinterpretation and maximize their potential.

Artificial intelligence (AI) is revolutionizing healthcare by providing tools for disease prediction, diagnosis, and patient management. This review focuses on two key AI methodologies in healthcare: predictive modeling and causal inference. Predictive models excel in identifying patterns to forecast outcomes but are limited in explaining the underlying causes. In contrast, causal inference focuses on understanding cause-and-effect relationships, which makes effective medical interventions possible. Although randomized controlled trials (RCTs) are the gold standard for causal inference, they face limitations including cost and ethical concerns. As alternatives, emulated RCTs and advanced machine learning techniques have emerged for estimating causal effects, bridging the gap between prediction and causality. Additionally, Shapley values and Local Interpretable Model-Agnostic Explanations improve the interpretability of complex AI models, making them more actionable in clinical settings. Integrating prediction and causal inference holds great promise for advancing personalized medicine, enhancing patient outcomes, and optimizing healthcare delivery. However, careful application of AI tools is crucial to avoid misinterpretation and maximize their potential.

We present a case of continuous ambulatory peritoneal dialysis peritonitis caused by Citrobacter freundii complicated by a fungal infection with abscess formation. A 34-year-old woman was admitted to our hospital with abdominal pain. Isolate cultures were confirmed as Citrobacter freundii by DNA sequencing of the 16s ribosomal ribonucleic acid (RNA). Antibiotic therapy was ineffective and Candida tropicalis was isolated in follow-up blood cultures. We administered an antifungal agent and removed the peritoneal catheter. A sudden fever developed, and abdominal computed tomography showed intra-abdominal abscesses. Percutaneous drainage was performed, but no bacteria were cultured. After draining the abscesses, the patient recovered. Citrobacter species are unusual pathogens in peritonitis, and fungal peritonitis is a serious complication of bacterial peritonitis. Indwelling catheters should be removed and appropriate antibiotic therapy provided. Suspicion of a fungal infection combined with bacterial peritonitis will improve the prognosis of patients on peritoneal dialysis.
Abdominal Abscess ; Abdominal Pain ; Abscess ; Adult ; Bacteria ; Candida tropicalis ; Catheters ; Catheters, Indwelling ; Citrobacter ; Citrobacter freundii* ; Drainage ; Female ; Fever ; Follow-Up Studies ; Humans ; Peritoneal Dialysis ; Peritoneal Dialysis, Continuous Ambulatory* ; Peritonitis* ; Prognosis ; RNA ; RNA, Ribosomal, 16S* ; Sequence Analysis, DNA

Background/Aims: Hypertension is considered a risk factor in immunoglobulin A nephropathy (IgAN). However, after IgAN diagnosis, the relationship between early blood pressure control and renal prognosis remains unclear. This study aimed to analyze the association between the prognosis of IgAN patients and a controlled status of hypertension within the first year of IgAN diagnosis. Methods: We retrospectively analyzed 2,945 patients diagnosed with IgAN by renal biopsy. The patients were divided into ‘normal,’ ‘new-onset,’ ‘well-controlled,’ and ‘poorly-controlled’ groups using blood pressure data from two consecutive measurements performed within a year. The Kaplan-Meier survival analysis and Cox proportional-hazards regression model were used to survey the independent association between recovery from hypertension and the risk of IgAN progression. The primary endpoint was IgAN progression defined as the initiation of dialysis or kidney transplantation. Results: Before IgAN diagnosis, 1,239 patients (42.1%) had been diagnosed with hypertension. In the fully adjusted Cox proportional-hazards models, the risk of IgAN progression increased by approximately 1.7-fold for the prevalence of hypertension. In the subgroup analyses, the ‘well-controlled’ group showed a statistically significant risk of IgAN progression (hazard ratio [HR], 3.19; 95% confidence interval [CI], 1.103 to 9.245; p = 0.032). Moreover, the ‘new-onset’ and ‘poorly-controlled’ groups had an increased risk of IgAN progression compared to the ‘normal’ group (HR, 2.58; 95% CI, 1.016 to 6.545; p = 0.046 and HR, 3.85;95% CI, 1.541 to 9.603; p = 0.004, respectively). Conclusions Although hypertension was well-controlled in the first year after IgAN diagnosis, it remained a risk factor for IgAN progression.

We report a case of severe hyperphosphatemia in advanced CKD with poor compliance. A 55-year-old male patient with underlying type 2 diabetes mellitus, hypertension, and chronic kidney disease presented emergently with general weakness and altered mental status. The creatinine level was 14 mg/dL (normal range: 0.5-1.3 mg/dL) 2 months prior to consultation, and he was advised initiation of hemodialysis, which he refused. Subsequently, the patient stopped taking all prescribed medications and self-medicated with honey and persimmon vinegar with the false belief it was detoxifying. At the time of admission, he was delirious, and his laboratory results showed blood urea nitrogen level of 183.4 mg/dL (8-23 mg/dL), serum creatinine level of 26.61 mg/dL (0.5-1.3 mg/dL), serum phosphate level of 19.3 mg/dL (2.5-5.5 mg/dL), total calcium level of 4.3 mg/dL (8.4-10.2 mg/dL), vitamin D (25(OH)D) level of 5.71 ng/mL (30-100 ng/mL) and parathyroid hormone level of 401 pg/ml (9-55 pg/mL). Brain computed tomography revealed non-traumatic spontaneous subdural hemorrhage, presumably due to uremic bleeding.Emergent hemodialysis was initiated, and hyperphosphatemia and hypocalcemia were rectified; calcium acetate and cholecalciferol were administered. The patient’s general condition and laboratory results improved following dialysis. Strict dietary restrictions with patient education were implemented. Multifaceted interventions, including dietary counseling, administration of phosphate-lowering drugs, and lifestyle modifications, should be implemented when encountering patients with CKD, considering the extent of the patient’s adherence.

Background: Hypertension is the most important modifiable risk factor for mortality and morbidity in chronic kidney disease and coronary artery syndrome. The effect of hypertension prior to percutaneous coronary intervention (PCI) on the development of end-stage renal disease (ESRD) is unknown. Methods: We used nationally representative data from the Korean National Health Insurance System—140,164 subjects were enrolled during 2010–2015; they were free of ESRD at enrolment, underwent PCI, and were followed up until 2017. Blood pressure (BP) was measured within at least 2 years prior to PCI. The primary outcome was the development of ESRD. Results: During a median follow-up of 5.4 years, 2,082 participants (1.5%) developed ESRD. The highest systolic BP group (>160 mmHg) showed a higher hazard ratio (3.69; 95% confidence interval, 2.61–5.23) than the reference group (110–119 mmHg). Similar results were observed in the highest diastolic BP group (>120 mmHg), which showed a higher hazard ratio than the reference group (70–79 mmHg). However, ESRD risk showed a J-shaped relationship with baseline systolic and diastolic BP at 113 and 74 mmHg in diabetes mellitus subgroup, respectively, after adjustment for potential confounders. Conclusion Our study showed that a high systolic or diastolic BP prior to PCI was independently associated with an increased incidence of ESRD.