Subtyping Hyperchloremia among Hospitalized Patients by Machine Learning Consensus Clustering
, Pattharawin Pattharanitima 3, Michael A. Mao 4, Andrea G. Kattah 1, Mira T. Keddis 5, Carissa Y. Dumancas 1, Saraschandra Vallabhajosyula 6, Tananchai Petnak 7, Stephen B. Erickson 1, John J. Dillon 1, Vesna D. Garovic 1, Kianoush B. Kashani 1,7 and Wisit Cheungpasitporn 1,*Round 1
Reviewer 1 Report
The article is interesting for a narrow group of people. The study was carried out correctly. Interesting article. I recommend his publications.
Author Response
The article is interesting for a narrow group of people. The study was carried out correctly. Interesting article. I recommend his publications.
Response: We thank you for reviewing our manuscript and for your critical evaluation.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The purpose of the study was, based on laboratory data from patients with hyponatremia on admission to hospital, to evaluate the short- and long-term prognosis of mortality. More than 11,000 patients with hyponatremia were identified through an unsupervised learning machine approach and divided into related cluster. Three main groups have been identified. Cluster 1: patients with urinary tract infection on admission, diabetes and advanced chronic kidney disease. Cluster 2: Older patients, most with respiratory disease on admission and associated CAD, CHF, stroke and COPD and the lowest level of hyponatremia. Cluster 3: Younger patients, lower degree of comorbidities and better renal function. In a glance evaluation, less informed physicians would bet that the worst prognosis would be cluster 2, related to cardiovascular complications much more frequent and with greater notoriety. The final result showed that cluster 1 had a worse prognosis. The merit of the study was to use an impersonal instrument, with the aid of science (mathematics?) using simple and easily obtainable variables, was possible to make predictions from preliminary data on hospital admission based on the presence of hyponatremia. It should be taken into account that hyponatremia on admission may have been neglected. Patients with hyponatremia and a previous history of cardiovascular complications would be intuitively classified as having a worse prognosis. On the contrary, those with severe and irreversible renal complications had the worst prognosis. Perhaps because being asymptomatic and of the worst knowledge. of doctors in general about kidney disease these complications are less valued. Initiatives such as these, to make clinical prognoses easy to be obtained in hospital admissions through impersonal scientific instruments, can be a warning path with reasonable antecedence so that such patients can be followed up with greater attention in the future.
Author Response
Reviewer 2
The purpose of the study was, based on laboratory data from patients with hyperchloremic on admission to hospital, to evaluate the short- and long-term prognosis of mortality. More than 11,000 patients with hyperchloremic were identified through an unsupervised learning machine approach and divided into related cluster. Three main groups have been identified. Cluster 1: patients with urinary tract infection on admission, diabetes and advanced chronic kidney disease. Cluster 2: Older patients, most with respiratory disease on admission and associated CAD, CHF, stroke and COPD and the lowest level of hyperchloremic. Cluster 3: Younger patients, lower degree of comorbidities and better renal function. In a glance evaluation, less informed physicians would bet that the worst prognosis would be cluster 2, related to cardiovascular complications much more frequent and with greater notoriety. The final result showed that cluster 1 had a worse prognosis. The merit of the study was to use an impersonal instrument, with the aid of science (mathematics?) using simple and easily obtainable variables, was possible to make predictions from preliminary data on hospital admission based on the presence of hyperchloremic. It should be taken into account that hyperchloremic on admission may have been neglected. Patients with hyperchloremic and a previous history of cardiovascular complications would be intuitively classified as having a worse prognosis. On the contrary, those with severe and irreversible renal complications had the worst prognosis. Perhaps because being asymptomatic and of the worst knowledge. of doctors in general about kidney disease these complications are less valued. Initiatives such as these, to make clinical prognoses easy to be obtained in hospital admissions through impersonal scientific instruments, can be a warning path with reasonable antecedence so that such patients can be followed up with greater attention in the future.
Response: We thank you for reviewing our manuscript and for your critical evaluation. We appreciate the reviewer’s kind comments and thus we additionally included important points that the reviewer raised in the discussion of our manuscript as suggested.
“Nonetheless, identifying distinct phenotypes in patients with hyperchloremia may pro-vide potential implications for managing and following patients with hyperchloremia, so that such hyperchloremic patients with high-risk mortality can be followed up with greater attention, and future studies are required to evaluate the application of this approach to clinical practice.”
Author Response File: Author Response.pdf
